Instructions to use sahilchachra/Leanstral-1.5-119B-A6B-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sahilchachra/Leanstral-1.5-119B-A6B-NVFP4 with Transformers:

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

pipe = pipeline("image-text-to-text", model="sahilchachra/Leanstral-1.5-119B-A6B-NVFP4")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("sahilchachra/Leanstral-1.5-119B-A6B-NVFP4")
model = AutoModelForMultimodalLM.from_pretrained("sahilchachra/Leanstral-1.5-119B-A6B-NVFP4")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sahilchachra/Leanstral-1.5-119B-A6B-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sahilchachra/Leanstral-1.5-119B-A6B-NVFP4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sahilchachra/Leanstral-1.5-119B-A6B-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/sahilchachra/Leanstral-1.5-119B-A6B-NVFP4

SGLang

How to use sahilchachra/Leanstral-1.5-119B-A6B-NVFP4 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 "sahilchachra/Leanstral-1.5-119B-A6B-NVFP4" \
    --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": "sahilchachra/Leanstral-1.5-119B-A6B-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "sahilchachra/Leanstral-1.5-119B-A6B-NVFP4" \
        --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": "sahilchachra/Leanstral-1.5-119B-A6B-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use sahilchachra/Leanstral-1.5-119B-A6B-NVFP4 with Docker Model Runner:
```
docker model run hf.co/sahilchachra/Leanstral-1.5-119B-A6B-NVFP4
```

Leanstral-1.5-119B-A6B — NVFP4

NVFP4 (4-bit) quantization of Leanstral-1.5-119B-A6B for Blackwell-class NVIDIA GPUs (native FP4 tensor cores). Derived from the BF16 HF conversion sahilchachra/Leanstral-1.5-119B-A6B-BF16, itself an unofficial community conversion of mistralai/Leanstral-1.5-119B-A6B.

Architecture

Composed Mistral3 model = DeepSeek-V3 text backbone (MLA attention + shared-expert MoE: 128 routed experts, 4 active/token, 1 shared) + Pixtral vision encoder. (llama.cpp also detects the text stack as deepseek2 = DeepSeek-V3.)

What was quantized

NVFP4 (W4A4), nvfp4-pack-quantized, group 16, FP8-E4M3 scales: all language-model Linear layers and all MoE experts — the fused DeepSeek-V3 experts are auto-linearized by llm-compressor and packed. 13,932 expert tensors packed (verified — experts were NOT skipped).
Kept full precision: vision tower, multimodal projector, MoE router (mlp.gate), lm_head, token embeddings.
Calibration: HuggingFaceH4/ultrachat_200k, 128 samples, seq 512, text-only.

Provenance & precision

Pipeline: original FP8 consolidated → dequant to BF16 (lossless) → NVFP4. The 4-bit weights derive from an already-8-bit source, not full-precision weights.

✅ Verified / ⚠️ Not yet verified (tested 2026-07-04 on 1× B200, CUDA 13)

✅ Conversion is structurally exact: strict key+shape match against a real Mistral3(deepseek_v3+pixtral) model (801 tensors).
✅ NVFP4 produced with all experts packed (13,932 tensors); valid compressed-tensors format; loads in transformers.
✅ vLLM 0.23 resolves the architecture (Mistral3ForConditionalGeneration).
⚠️ End-to-end generation not yet confirmed on a single B200. Two engine-side gaps for this new composed arch (deepseek_v3 text inside Mistral3):
- vLLM 0.23 raises KeyError: ...mlp.gate.e_score_correction_bias when loading weights (its DeepSeek-V3 loader in the Mistral3 wrapper expects a different key path). Needs a vLLM build that supports this composition.
- transformers loads it but dequantizes NVFP4 to BF16 (no FP4 compute path) → ~159 GB + the naive MoE loop → OOM/too slow on one 183 GB B200.
⚠️ Vision path is converted (Pixtral tensors + preprocessor_config.json present) but not runtime-validated.
⚠️ RoPE scaling is approximate (YARN mapped; source also carries llama_4_scaling). Long-context (>8k) not guaranteed.

In short: the artifact is correct; serving it needs engine support for the composed arch that isn't in the pinned stack yet. Track vLLM support for DeepSeek-V3-backed Mistral3.

Intended usage (mirrors the base model)

Tokenizer is Tekken (tekken.json, use mistral_common); chat format is the Mistral instruct format (handled by mistral_common, no manual [INST]). The base model recommends temperature 1.0. Once engine support lands, the intended path:

vllm serve sahilchachra/Leanstral-1.5-119B-A6B-NVFP4 \
  --tokenizer-mode mistral --reasoning-parser mistral \
  --tool-call-parser mistral --enable-auto-tool-choice

Files included: config.json, weights + index, tekken.json, params.json, chat_template.jinja, preprocessor_config.json.

License

Derives from mistralai/Leanstral-1.5-119B-A6B; upstream license/terms apply. Community artifact, not affiliated with Mistral AI.

Downloads last month: 357

Safetensors

Model size

68B params

Tensor type

F32

BF16

F8_E4M3

Model tree for sahilchachra/Leanstral-1.5-119B-A6B-NVFP4

Base model

mistralai/Leanstral-2603

Finetuned

mistralai/Leanstral-1.5-119B-A6B

Quantized

(4)

this model