Instructions to use trl-internal-testing/tiny-MistralLarge4ForConditionalGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use trl-internal-testing/tiny-MistralLarge4ForConditionalGeneration with Transformers:

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

pipe = pipeline("image-text-to-text", model="trl-internal-testing/tiny-MistralLarge4ForConditionalGeneration")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("trl-internal-testing/tiny-MistralLarge4ForConditionalGeneration")
model = AutoModelForMultimodalLM.from_pretrained("trl-internal-testing/tiny-MistralLarge4ForConditionalGeneration", device_map="auto")
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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use trl-internal-testing/tiny-MistralLarge4ForConditionalGeneration with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "trl-internal-testing/tiny-MistralLarge4ForConditionalGeneration"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "trl-internal-testing/tiny-MistralLarge4ForConditionalGeneration",
		"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/trl-internal-testing/tiny-MistralLarge4ForConditionalGeneration

SGLang

How to use trl-internal-testing/tiny-MistralLarge4ForConditionalGeneration 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 "trl-internal-testing/tiny-MistralLarge4ForConditionalGeneration" \
    --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": "trl-internal-testing/tiny-MistralLarge4ForConditionalGeneration",
		"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 "trl-internal-testing/tiny-MistralLarge4ForConditionalGeneration" \
        --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": "trl-internal-testing/tiny-MistralLarge4ForConditionalGeneration",
		"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 trl-internal-testing/tiny-MistralLarge4ForConditionalGeneration with Docker Model Runner:
```
docker model run hf.co/trl-internal-testing/tiny-MistralLarge4ForConditionalGeneration
```

Upload MistralLarge4ForConditionalGeneration

by albertvillanova HF Staff - opened 3 days ago

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

+11

-12

Files changed (4) hide show

config.json +1 -7
generation_config.json +6 -1
model.safetensors +2 -2
tokenizer_config.json +2 -2

config.json CHANGED Viewed

@@ -9,9 +9,6 @@
   "projector_hidden_act": "gelu",
   "quantization_config": {
     "activation_scheme": "dynamic",
-    "dequantize": false,
-    "expert_dtype": "fp8",
-    "modules_to_convert": null,
     "modules_to_not_convert": [
       "model.vision_tower",
       "model.multi_modal_projector",
@@ -22,7 +19,6 @@
       "kv_a_proj_with_mqa"
     ],
     "quant_method": "fp8",
-    "scale_fmt": "float",
     "weight_block_size": [
       32,
       32
@@ -33,7 +29,6 @@
     "attention_bias": false,
     "attention_dropout": 0.0,
     "bos_token_id": 1,
-    "dtype": "bfloat16",
     "eos_token_id": 2,
     "final_logit_softcapping": 30.0,
     "first_k_dense_replace": 1,
@@ -130,10 +125,9 @@
     "vocab_size": 204800
   },
   "tie_word_embeddings": false,
-  "transformers_version": "5.21.0.dev0",
   "vision_config": {
     "attention_dropout": 0.0,
-    "dtype": "bfloat16",
     "head_dim": 4,
     "hidden_act": "silu",
     "hidden_size": 16,

   "projector_hidden_act": "gelu",
   "quantization_config": {
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     "final_logit_softcapping": 30.0,
     "first_k_dense_replace": 1,
     "vocab_size": 204800
   },
   "tie_word_embeddings": false,
+  "transformers_version": "5.22.0",
   "vision_config": {
     "attention_dropout": 0.0,
     "head_dim": 4,
     "hidden_act": "silu",
     "hidden_size": 16,

generation_config.json CHANGED Viewed

@@ -1,5 +1,10 @@
 {
   "bos_token_id": 1,
   "eos_token_id": 2,
-  "transformers_version": "5.21.0.dev0"
 }

 {
   "bos_token_id": 1,
+  "do_sample": true,
   "eos_token_id": 2,
+  "max_length": 1048576,
+  "pad_token_id": 11,
+  "temperature": 1.0,
+  "top_p": 0.95,
+  "transformers_version": "5.22.0"
 }

model.safetensors CHANGED Viewed

@@ -1,3 +1,3 @@
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tokenizer_config.json CHANGED Viewed

@@ -2,9 +2,9 @@
   "backend": "tokenizers",
   "bos_token": "<s>",
   "eos_token": "</s>",
-  "is_local": true,
   "local_files_only": false,
-  "model_max_length": 1000000000000000019884624838656,
   "pad_token": "<pad>",
   "processor_class": "PixtralProcessor",
   "tokenizer_class": "TokenizersBackend",

   "backend": "tokenizers",
   "bos_token": "<s>",
   "eos_token": "</s>",
+  "is_local": false,
   "local_files_only": false,
+  "model_max_length": 1048576,
   "pad_token": "<pad>",
   "processor_class": "PixtralProcessor",
   "tokenizer_class": "TokenizersBackend",