Instructions to use eigentom/nanocode-sft-mix-run1-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use eigentom/nanocode-sft-mix-run1-v3 with Transformers:

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

pipe = pipeline("text-generation", model="eigentom/nanocode-sft-mix-run1-v3")
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("eigentom/nanocode-sft-mix-run1-v3")
model = AutoModelForCausalLM.from_pretrained("eigentom/nanocode-sft-mix-run1-v3", 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 eigentom/nanocode-sft-mix-run1-v3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "eigentom/nanocode-sft-mix-run1-v3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "eigentom/nanocode-sft-mix-run1-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/eigentom/nanocode-sft-mix-run1-v3

SGLang

How to use eigentom/nanocode-sft-mix-run1-v3 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 "eigentom/nanocode-sft-mix-run1-v3" \
    --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": "eigentom/nanocode-sft-mix-run1-v3",
		"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 "eigentom/nanocode-sft-mix-run1-v3" \
        --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": "eigentom/nanocode-sft-mix-run1-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use eigentom/nanocode-sft-mix-run1-v3 with Docker Model Runner:
```
docker model run hf.co/eigentom/nanocode-sft-mix-run1-v3
```

MiniCPM5 SFT mix Run1 v3

Final checkpoint from a fresh one-epoch full SFT of MiniCPM5-2B-Midtrain on the v3 filtered Run1 data mix. This is checkpoint-5913, not a continuation of the old Run1 checkpoints.

Base revision: 0a45344e; base weight SHA-256: 38a28680f6208242a0de7c84627343d44cee517b49c2b39d1afd706be6beabad.
LLaMA-Factory frontend, Megatron/MCore adapter, 12 nodes / 192 Ascend 910C cards; TP4, DP48, micro batch 1, accumulation4, global batch192.
One epoch, 5913 updates; LR5e-5 cosine to1e-5, warmup3%, weight decay0.01, seed20260916, BF16.
Maximum training sequence131072; packed and balanced scheduling. Assistant header masking repaired; source labels remain at the same positions and the MCore collator shifts once.
Packed input tokens:36,161,705,387; supervised tokens:15,591,318,474. These describe the tokenized dataset, not an independent runtime token counter.
Final held-out validation loss:0.2753264904022217. SWE evaluation of this checkpoint is pending; training loss alone is not a benchmark result.

HF BF16 weights were converted once on node0 without modifying the original MCore checkpoint. Structural config, tokenizer, RoPE semantics and safetensors shards were checked. conversion-receipt.json records hashes and source provenance. Model uses XML-style MiniCPM5 tool calling; use the MiniCPM5 parser and the provided chat template.

Related fixed-200 evaluation task set: eigentom/minicpm5-sft-swe-validation-200. Evaluation results will be reported separately using the same frozen protocol as the baseline runs.

Downloads last month: 184

Safetensors

Model size

3B params

Tensor type

F32

BF16

Model tree for eigentom/nanocode-sft-mix-run1-v3

Base model

openbmb/MiniCPM5-2B-Midtrain

Finetuned

(6)

this model