Release step 1000 with tokenizer, evaluation results and model card

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,71 @@
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NOTICE

@@ -0,0 +1,10 @@
+Base model: LiquidAI/LFM2.5-2.6B
+Base revision: 654f9463ce32b05d0429d76fe1f580b27d4c1ac0
+Original model and license: Liquid AI, Inc.
+
+FineEnvs fine-tuned model.safetensors using TRL Async GRPO.
+Checkpoint: 1000; release revision: main.
+Configuration, generation settings, tokenizer serialization and chat template
+are the saved training-checkpoint versions and may differ from the base release.
+The original license is retained in LICENSE.
+Experiment: https://huggingface.co/spaces/AdithyaSK/multi-harness-rl

README.md

@@ -0,0 +1,182 @@
+---
+base_model: LiquidAI/LFM2.5-2.6B
+base_model_relation: finetune
+library_name: transformers
+pipeline_tag: text-generation
+language:
+- en
+license: other
+tags:
+- trl
+- openenv
+- harbor
+- agent
+- smoldataenvs
+- grpo
+- multi-harness
+datasets:
+- FineEnvs/SmolDataEnvs-harbor-train
+thumbnail: https://huggingface.co/spaces/AdithyaSK/multi-harness-rl/resolve/main/app/public/og/og-image.png
+model-index:
+- name: LFM2.5-2.6B-multiharness-RL (main, step 1000)
+  results:
+  - task:
+      type: text-generation
+      name: Agentic data analysis
+    dataset:
+      type: FineEnvs/SmolDataEnvs-harbor-test
+      name: SmolDataEnvs (250 fixed test tasks)
+      split: test
+      args:
+        harness: all four
+    metrics:
+    - type: pass_at_1
+      name: Pass@1 (%) — Overall
+      value: 54.2
+  - task:
+      type: text-generation
+      name: Agentic data analysis
+    dataset:
+      type: FineEnvs/SmolDataEnvs-harbor-test
+      name: SmolDataEnvs (250 fixed test tasks)
+      split: test
+      args:
+        harness: opencode
+    metrics:
+    - type: pass_at_1
+      name: Pass@1 (%) — OpenCode
+      value: 49.6
+  - task:
+      type: text-generation
+      name: Agentic data analysis
+    dataset:
+      type: FineEnvs/SmolDataEnvs-harbor-test
+      name: SmolDataEnvs (250 fixed test tasks)
+      split: test
+      args:
+        harness: claude-code
+    metrics:
+    - type: pass_at_1
+      name: Pass@1 (%) — Claude Code
+      value: 48.8
+  - task:
+      type: text-generation
+      name: Agentic data analysis
+    dataset:
+      type: FineEnvs/SmolDataEnvs-harbor-test
+      name: SmolDataEnvs (250 fixed test tasks)
+      split: test
+      args:
+        harness: codex
+    metrics:
+    - type: pass_at_1
+      name: Pass@1 (%) — Codex
+      value: 53.6
+  - task:
+      type: text-generation
+      name: Agentic data analysis
+    dataset:
+      type: FineEnvs/SmolDataEnvs-harbor-test
+      name: SmolDataEnvs (250 fixed test tasks)
+      split: test
+      args:
+        harness: mini-swe-agent
+    metrics:
+    - type: pass_at_1
+      name: Pass@1 (%) — Mini-SWE-Agent
+      value: 64.8
+license_name: lfm1.0
+license_link: LICENSE
+---
+
+[![The ultimate guide to multi-harness RL](https://huggingface.co/spaces/AdithyaSK/multi-harness-rl/resolve/main/app/public/og/og-image.png)](https://huggingface.co/spaces/AdithyaSK/multi-harness-rl)
+
+# LFM2.5-2.6B-multiharness-RL
+
+A full fine-tune of [LiquidAI/LFM2.5-2.6B](https://huggingface.co/LiquidAI/LFM2.5-2.6B) for agentic data-analysis tasks, trained with asynchronous GRPO (TRL Async GRPO) using **OpenCode, Claude Code, Codex, Mini-SWE-Agent**. This release is **step 1,000** on **`main`**, scoring **54.2% pass@1** across four evaluation harnesses.
+
+[Article](https://huggingface.co/spaces/AdithyaSK/multi-harness-rl) · [Collection](https://huggingface.co/collections/FineEnvs/multi-harness-rl-6abdfaaa8d74dacd481d5212) · [Evaluation tasks](https://huggingface.co/datasets/FineEnvs/SmolDataEnvs-harbor-test) · [Training dashboard](https://huggingface.co/spaces/FineEnvs/data-agent-training-comparison-trackio)
+
+## Training
+
+TRL Async GRPO with **correctness plus a tool-call efficiency bonus**, initialized from the base model. Each group uses one task and one harness; harnesses rotate across groups. Rollouts run through OpenEnv × Harbor in E2B sandboxes.
+
+| Setting | Value |
+|---|---|
+| Training task pool | 1,000 tasks: 400 medium / 600 hard |
+| Checkpoint | 1,000 optimizer steps |
+| Learning rate | 3e-6 |
+| Rollouts per GRPO group / maximum staleness | 8 / 4 optimizer steps |
+| Optimizer / precision | paged AdamW 8-bit / bfloat16 |
+| Training harnesses | OpenCode, Claude Code, Codex, Mini-SWE-Agent |
+| Sampling temperature / top-p | 0.8 / 1.0 |
+| Per-call output budget, training / evaluation | 4,096 / 4,096 tokens |
+
+The reward is `correctness × (1 + 0.1 × 15 / (15 + tool_calls))`, using verified native harness action counts. Incorrect answers receive zero; missing or unverified action counts receive no efficiency bonus. Reported pass@1 measures correctness only. The 1,000-task pool is not a claim that every task contributed an optimizer update.
+
+## Evaluation
+
+250 fixed SmolDataEnvs test tasks (33 easy, 118 medium, 99 hard), each evaluated under four harnesses: **1,000 graded task/harness cells**. Pass@1 uses the first graded attempt per cell; infrastructure retries do not turn it into pass@k. All cells below are graded.
+
+| Harness | Correct / evaluated | Pass@1 |
+|---|---:|---:|
+| OpenCode | 124 / 250 | 49.6% |
+| Claude Code | 122 / 250 | 48.8% |
+| Codex | 134 / 250 | 53.6% |
+| Mini-SWE-Agent | 162 / 250 | 64.8% |
+| **Overall** | **542 / 1,000** | **54.2%** |
+
+Harness versions: OpenCode 1.18.31, Claude Code 2.1.270, Codex 0.154.0, Mini-SWE-Agent 2.4.6. Full scores, including difficulty breakdowns, are in [eval_results.json](eval_results.json).
+
+The [main branch](https://huggingface.co/FineEnvs/LFM2.5-2.6B-multiharness-RL/tree/main) contains the final step-1,000 checkpoint (54.2%). The [step-700 branch](https://huggingface.co/FineEnvs/LFM2.5-2.6B-multiharness-RL/tree/step-700) contains the best observed checkpoint (54.6%), used in the article's SFT-versus-RL comparison. **Best checkpoint selection used this test set**, not a separate validation set.
+
+At step 1,000, tool calls were **31.1% lower** than the base model across **356 task/harness pairs solved by both**. This is a matched-success comparison, not a reduction across every task or a causal estimate of the reward's effect.
+
+These are single-run results on a specific task set and harness versions. Harness mix, training exposure and compute differ across runs; the scores do not isolate a causal effect of the harness or objective.
+
+## Load the checkpoint
+
+The repository contains full saved model weights and the saved tokenizer/chat template, not a LoRA adapter. Training used Transformers 5.14.1; use a compatible Transformers release.
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_id = "FineEnvs/LFM2.5-2.6B-multiharness-RL"
+revision = "main"
+tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id, revision=revision, dtype=torch.bfloat16, device_map="auto"
+)
+```
+
+Reproducing the task scores requires the agent harness and tools described in the [article](https://huggingface.co/spaces/AdithyaSK/multi-harness-rl); a plain chat prompt is not the same evaluation. 
+
+## Related releases
+
+- [LFM2.5-2.6B-multiharness-RL](https://huggingface.co/FineEnvs/LFM2.5-2.6B-multiharness-RL)
+- [LFM2.5-2.6B-opencode-RL](https://huggingface.co/FineEnvs/LFM2.5-2.6B-opencode-RL)
+- [LFM2.5-2.6B-multiharness-SFT](https://huggingface.co/FineEnvs/LFM2.5-2.6B-multiharness-SFT)
+- [LFM2.5-2.6B-opencode-SFT](https://huggingface.co/FineEnvs/LFM2.5-2.6B-opencode-SFT)
+- [Qwen3.5-2B-multiharness-RL](https://huggingface.co/FineEnvs/Qwen3.5-2B-multiharness-RL)
+- [Qwen3.5-2B-opencode-RL](https://huggingface.co/FineEnvs/Qwen3.5-2B-opencode-RL)
+- [Qwen3.5-2B-opencode-standalone-RL](https://huggingface.co/FineEnvs/Qwen3.5-2B-opencode-standalone-RL)
+
+All models, datasets, environments and the article are linked in the [multi-harness RL collection](https://huggingface.co/collections/FineEnvs/multi-harness-rl-6abdfaaa8d74dacd481d5212).
+
+## License and provenance
+
+Derived from [LiquidAI/LFM2.5-2.6B](https://huggingface.co/LiquidAI/LFM2.5-2.6B) under the LFM Open License v1.0. The base model's license is included unchanged in [LICENSE](LICENSE). FineEnvs modified the weights by reinforcement learning; this is not an official Liquid AI release. See [NOTICE](NOTICE) and [release_manifest.json](release_manifest.json) for modification notices, the pinned base revision, checkpoint identity and file checksums. Optimizer, scheduler, RNG and trainer state are excluded.
+
+## Citation
+
+For the experiment, methodology and interpretation, cite the [main article](https://huggingface.co/spaces/AdithyaSK/multi-harness-rl):
+
+```bibtex
+@misc{kolavi2026multiharnessrl,
+  author = {Adithya S Kolavi},
+  title = {The ultimate guide to multi-harness RL},
+  year = {2026},
+  url = {https://huggingface.co/spaces/AdithyaSK/multi-harness-rl}
+}
+```

chat_template.jinja

@@ -0,0 +1,125 @@
+{{- bos_token -}}
+{%- set preserve_thinking = preserve_thinking | default(false) -%}
+
+{%- macro format_arg_value(arg_value) -%}
+    {%- if arg_value is string -%}
+        {{- "'" + (arg_value | replace("\\", "\\\\") | replace("'", "\\'") | replace("\n", "\\n") | replace("\r", "\\r")) + "'" -}}
+    {%- elif arg_value is mapping or arg_value is iterable -%}
+        {{- arg_value | tojson -}}
+    {%- else -%}
+        {{- arg_value | string -}}
+    {%- endif -%}
+{%- endmacro -%}
+
+{%- macro parse_content(content) -%}
+    {%- if content is string -%}
+        {{- content -}}
+    {%- elif content is mapping -%}
+        {{- content | tojson -}}
+    {%- elif content is iterable -%}
+        {%- set _ns = namespace(result="") -%}
+        {%- for item in content -%}
+            {%- if item is string -%}
+                {%- set _ns.result = _ns.result + item -%}
+            {%- elif item is mapping and item.get("type") == "image" -%}
+                {%- set _ns.result = _ns.result + "<image>" -%}
+            {%- elif item is mapping and item.get("type") == "text" -%}
+                {%- set _ns.result = _ns.result + ((item.get("text") or "") | string) -%}
+            {%- else -%}
+                {%- set _ns.result = _ns.result + (item | tojson) -%}
+            {%- endif -%}
+        {%- endfor -%}
+        {{- _ns.result -}}
+    {%- endif -%}
+{%- endmacro -%}
+
+{%- macro render_tool_calls(tool_calls) -%}
+    {%- set tool_calls_ns = namespace(tool_calls=[]) -%}
+    {%- for tool_call in tool_calls -%}
+        {%- set func = tool_call["function"] if "function" in tool_call else tool_call -%}
+        {%- set func_name = func["name"] -%}
+        {%- set func_args = func.get("arguments") -%}
+        {%- set args_ns = namespace(arg_strings=[]) -%}
+        {%- if func_args is mapping -%}
+            {%- for arg_name, arg_value in func_args.items() -%}
+                {%- set args_ns.arg_strings = args_ns.arg_strings + [arg_name + "=" + format_arg_value(arg_value)] -%}
+            {%- endfor -%}
+        {%- elif func_args is string and (func_args | trim) not in ["", "{}", "null"] -%}
+            {{- raise_exception("Tool call arguments must be a mapping, got a JSON-encoded string: parse arguments with json.loads() before applying the chat template") -}}
+        {%- endif -%}
+        {%- set tool_calls_ns.tool_calls = tool_calls_ns.tool_calls + [func_name + "(" + (args_ns.arg_strings | join(", ")) + ")"] -%}
+    {%- endfor -%}
+    {{- "<|tool_call_start|>[" + (tool_calls_ns.tool_calls | join(", ")) + "]<|tool_call_end|>" -}}
+{%- endmacro -%}
+
+{%- set ns = namespace(system_prompt="", last_user_index=-1) -%}
+{%- if messages and messages[0]["role"] == "system" -%}
+    {%- if messages[0].get("content") -%}
+        {%- set ns.system_prompt = parse_content(messages[0]["content"]) -%}
+    {%- endif -%}
+    {%- set messages = messages[1:] -%}
+{%- endif -%}
+{%- if tools -%}
+    {%- set ns.system_prompt = ns.system_prompt + ("\n" if ns.system_prompt else "") + "List of tools: [" -%}
+    {%- for tool in tools -%}
+        {%- if tool is not string -%}
+            {%- set tool = tool | tojson -%}
+        {%- endif -%}
+        {%- set ns.system_prompt = ns.system_prompt + tool -%}
+        {%- if not loop.last -%}
+            {%- set ns.system_prompt = ns.system_prompt + ", " -%}
+        {%- endif -%}
+    {%- endfor -%}
+    {%- set ns.system_prompt = ns.system_prompt + "]" -%}
+{%- endif -%}
+{%- if ns.system_prompt -%}
+    {{- "<|im_start|>system\n" + ns.system_prompt + "<|im_end|>\n" -}}
+{%- endif -%}
+{%- for message in messages -%}
+    {%- if message["role"] == "user" -%}
+        {%- set ns.last_user_index = loop.index0 -%}
+    {%- endif -%}
+{%- endfor -%}
+{%- for message in messages -%}
+    {{- "<|im_start|>" + message.role + "\n" -}}
+    {%- if message.role == "assistant" -%}
+        {%- generation -%}
+        {%- set keep_thinking = preserve_thinking or loop.index0 > ns.last_user_index -%}
+        {%- set thinking = message.thinking or message.reasoning or message.reasoning_content -%}
+        {%- set thinking = thinking if thinking is string else "" -%}
+        {%- if thinking and keep_thinking -%}
+            {{- "<think>" + thinking + "</think>" -}}
+        {%- endif -%}
+        {%- set _cfm_tag = "CONTINUE_FINAL_MESSAGE_TAG " -%}
+        {%- set _has_cfm = false -%}
+        {%- set content = "" -%}
+        {%- if message.get("content") -%}
+            {%- set content = parse_content(message.content) -%}
+        {%- endif -%}
+        {%- if not keep_thinking and "</think>" in content -%}
+            {%- set content = content.split("</think>")[-1] | trim -%}
+        {%- endif -%}
+        {%- if content.endswith(_cfm_tag) -%}
+            {%- set _has_cfm = true -%}
+            {%- set _trunc_len = (content | length) - (_cfm_tag | length) -%}
+            {%- set content = content[:_trunc_len] -%}
+        {%- endif -%}
+        {{- content -}}
+        {%- if message.tool_calls -%}
+            {{- render_tool_calls(message.tool_calls) -}}
+        {%- endif -%}
+        {%- if _has_cfm -%}
+            {{- _cfm_tag -}}
+        {%- endif -%}
+        {{- "<|im_end|>\n" -}}
+        {%- endgeneration -%}
+    {%- else %}
+        {%- if message.get("content") -%}
+            {{- parse_content(message["content"]) -}}
+        {%- endif -%}
+        {{- "<|im_end|>\n" -}}
+    {%- endif %}
+{%- endfor -%}
+{%- if add_generation_prompt -%}
+    {{- "<|im_start|>assistant\n<think>" -}}
+{%- endif -%}

config.json

@@ -0,0 +1,74 @@
+{
+  "architectures": [
+    "Lfm2ForCausalLM"
+  ],
+  "block_auto_adjust_ff_dim": false,
+  "block_dim": 2048,
+  "block_ffn_dim_multiplier": 1.0,
+  "block_mlp_init_scale": 1.0,
+  "block_multiple_of": 256,
+  "block_norm_eps": 1e-05,
+  "block_out_init_scale": 1.0,
+  "block_use_swiglu": true,
+  "block_use_xavier_init": true,
+  "bos_token_id": 124894,
+  "conv_L_cache": 3,
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