Instructions to use FineEnvs/LFM2.5-2.6B-multiharness-RL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FineEnvs/LFM2.5-2.6B-multiharness-RL with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="FineEnvs/LFM2.5-2.6B-multiharness-RL")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FineEnvs/LFM2.5-2.6B-multiharness-RL")
model = AutoModelForCausalLM.from_pretrained("FineEnvs/LFM2.5-2.6B-multiharness-RL", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FineEnvs/LFM2.5-2.6B-multiharness-RL with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FineEnvs/LFM2.5-2.6B-multiharness-RL"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FineEnvs/LFM2.5-2.6B-multiharness-RL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FineEnvs/LFM2.5-2.6B-multiharness-RL

SGLang

How to use FineEnvs/LFM2.5-2.6B-multiharness-RL with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "FineEnvs/LFM2.5-2.6B-multiharness-RL" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FineEnvs/LFM2.5-2.6B-multiharness-RL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "FineEnvs/LFM2.5-2.6B-multiharness-RL" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FineEnvs/LFM2.5-2.6B-multiharness-RL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FineEnvs/LFM2.5-2.6B-multiharness-RL with Docker Model Runner:
```
docker model run hf.co/FineEnvs/LFM2.5-2.6B-multiharness-RL
```

AdithyaSK HF Staff commited on 29 days ago

Commit

3c1e352

verified ·

1 Parent(s): 429a845

Fix disconnected evaluation lines and GIF previews

Browse files

Files changed (3) hide show

README.md +2 -2
assets/curves.gif +2 -2
assets/curves.png +2 -2

README.md CHANGED Viewed

@@ -99,9 +99,9 @@ A full fine-tune of [LiquidAI/LFM2.5-2.6B](https://huggingface.co/LiquidAI/LFM2.
 ## Training and evaluation curves
-[![Animated training, pass@1 and tool-use curves through step 1,000](https://huggingface.co/FineEnvs/LFM2.5-2.6B-multiharness-RL/resolve/main/assets/curves.gif)](https://huggingface.co/spaces/AdithyaSK/multi-harness-rl)
-Training curves use a trailing 50-step mean (at least 10 observations). Evaluation markers show measured checkpoints; hollow markers indicate incomplete coverage. Tool-call savings compare tasks solved by both the base model and checkpoint, with a different matched cohort at each checkpoint.  The animation stops at this revision's step 1,000.
 [Static chart](https://huggingface.co/FineEnvs/LFM2.5-2.6B-multiharness-RL/resolve/main/assets/curves.png) · [Plotted data](https://huggingface.co/FineEnvs/LFM2.5-2.6B-multiharness-RL/resolve/main/assets/curves.json) · [Interactive article](https://huggingface.co/spaces/AdithyaSK/multi-harness-rl)

 ## Training and evaluation curves
+[![Animated training, pass@1 and tool-use curves through step 1,000](https://huggingface.co/FineEnvs/LFM2.5-2.6B-multiharness-RL/resolve/main/assets/curves.gif?v=2)](https://huggingface.co/spaces/AdithyaSK/multi-harness-rl)
+Training curves use a trailing 50-step mean (at least 10 observations). Evaluation markers show measured checkpoints; hollow markers and dotted segments indicate incomplete coverage. Faint background lines show the full recorded trajectory; colored lines reveal the measured checkpoints. The first frame shows the completed chart before replaying, so previews also contain the full curves. Tool-call savings compare tasks solved by both the base model and checkpoint, with a different matched cohort at each checkpoint.  The animation stops at this revision's step 1,000.
 [Static chart](https://huggingface.co/FineEnvs/LFM2.5-2.6B-multiharness-RL/resolve/main/assets/curves.png) · [Plotted data](https://huggingface.co/FineEnvs/LFM2.5-2.6B-multiharness-RL/resolve/main/assets/curves.json) · [Interactive article](https://huggingface.co/spaces/AdithyaSK/multi-harness-rl)

assets/curves.gif CHANGED Viewed

Git LFS Details

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Pointer size: 132 Bytes
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Git LFS Details

SHA256: 4ccad903b37d506b80bb3ca6e447b1aa8a7ca23cd397f8aa4fc15d445f3c37f3
Pointer size: 132 Bytes
Size of remote file: 4.32 MB

assets/curves.png CHANGED Viewed

Git LFS Details

SHA256: c7aee7a1158e1c29cc6815e769cdc7fda06474d4183b8953d3074cf5a0341264
Pointer size: 131 Bytes
Size of remote file: 150 kB

Git LFS Details

SHA256: 4468aa6ab7328ca8fcdb461cd03f3df3311ab60cd9d92cfdf71150d71530c840
Pointer size: 131 Bytes
Size of remote file: 169 kB