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

Libraries

How to use FineEnvs/LFM2.5-2.6B-opencode-SFT with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FineEnvs/LFM2.5-2.6B-opencode-SFT")
model = AutoModelForCausalLM.from_pretrained("FineEnvs/LFM2.5-2.6B-opencode-SFT", 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=40)
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-opencode-SFT 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-opencode-SFT"
# 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-opencode-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FineEnvs/LFM2.5-2.6B-opencode-SFT

SGLang

How to use FineEnvs/LFM2.5-2.6B-opencode-SFT 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-opencode-SFT" \
    --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-opencode-SFT",
		"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-opencode-SFT" \
        --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-opencode-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FineEnvs/LFM2.5-2.6B-opencode-SFT with Docker Model Runner:
```
docker model run hf.co/FineEnvs/LFM2.5-2.6B-opencode-SFT
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

LFM2.5-2.6B-opencode-SFT

A full fine-tune of LiquidAI/LFM2.5-2.6B for agentic data-analysis tasks, trained with supervised fine-tuning (TRL SFT) using OpenCode. This release is step 1,208 on main, epoch 2, scoring 47.5% pass@1 across four evaluation harnesses.

Article · Collection · Evaluation tasks · Training dashboard

Training and evaluation curves

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. SFT evaluations were recorded at the end of each epoch only. The animation stops at this revision's step 1,208.

Static chart · Plotted data · Interactive article

Training

TRL SFT on FineEnvs/SmolDataEnvs-multiharness-sft, using the OpenCode subset only. The data contains successful Qwen3.8-27B teacher rollouts collected through OpenEnv × Harbor in Daytona sandboxes. Up to three teacher attempts were allowed per task/harness; the first successful trace was retained.

This run uses 4,825 assistant-turn examples from 801 successful rollouts covering 801 distinct tasks. Loss is applied only to the next assistant response; user messages, tool outputs and prior history are context. Responses are tokenized for LFM, not trained against teacher token IDs or logprobs.

Setting	Value
Epochs / checkpoint	2 / 1,208
Learning rate / schedule	3e-6 / constant
Effective batch size	8
Optimizer / precision	paged AdamW 8-bit / bfloat16
Training harnesses	OpenCode
Objective	Completion-only supervised fine-tuning

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	129 / 250	51.6%
Claude Code	83 / 250	33.2%
Codex	116 / 250	46.4%
Mini-SWE-Agent	147 / 250	58.8%
Overall	475 / 1,000	47.5%

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.

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.

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "FineEnvs/LFM2.5-2.6B-opencode-SFT"
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; a plain chat prompt is not the same evaluation.

Related releases

All models, datasets, environments and the article are linked in the multi-harness RL collection.

License and provenance

Derived from LiquidAI/LFM2.5-2.6B under the LFM Open License v1.0. The base model's license is included unchanged in LICENSE. FineEnvs modified the weights by supervised fine-tuning; this is not an official Liquid AI release. See NOTICE and 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:

@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}
}

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Safetensors

Model size

3B params

Tensor type

BF16

Model tree for FineEnvs/LFM2.5-2.6B-opencode-SFT

Base model

LiquidAI/LFM2.5-2.6B-Base

Finetuned

LiquidAI/LFM2.5-2.6B

Finetuned

(60)

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Quantizations

2 models

Dataset used to train FineEnvs/LFM2.5-2.6B-opencode-SFT

Collection including FineEnvs/LFM2.5-2.6B-opencode-SFT

SmolDataEnvs Multi-harness RL

Collection

Try the three environments, follow the tutorial, and explore the datasets, models and historical RL/SFT results. • 21 items • Updated about 15 hours ago • 2

Evaluation results

Pass@1 (%) — Overall on SmolDataEnvs (250 fixed test tasks)
test set self-reported

47.500
Pass@1 (%) — OpenCode on SmolDataEnvs (250 fixed test tasks)
test set self-reported

51.600
Pass@1 (%) — Claude Code on SmolDataEnvs (250 fixed test tasks)
test set self-reported

33.200
Pass@1 (%) — Codex on SmolDataEnvs (250 fixed test tasks)
test set self-reported

46.400
Pass@1 (%) — Mini-SWE-Agent on SmolDataEnvs (250 fixed test tasks)
test set self-reported

58.800