Instructions to use katuni4ka/tiny-random-mistral4-text-only with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use katuni4ka/tiny-random-mistral4-text-only with Transformers:

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

pipe = pipeline("image-text-to-text", model="katuni4ka/tiny-random-mistral4-text-only", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForImageTextToText

tokenizer = AutoTokenizer.from_pretrained("katuni4ka/tiny-random-mistral4-text-only", trust_remote_code=True)
model = AutoModelForImageTextToText.from_pretrained("katuni4ka/tiny-random-mistral4-text-only", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use katuni4ka/tiny-random-mistral4-text-only with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "katuni4ka/tiny-random-mistral4-text-only"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "katuni4ka/tiny-random-mistral4-text-only",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/katuni4ka/tiny-random-mistral4-text-only

SGLang

How to use katuni4ka/tiny-random-mistral4-text-only 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 "katuni4ka/tiny-random-mistral4-text-only" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "katuni4ka/tiny-random-mistral4-text-only",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "katuni4ka/tiny-random-mistral4-text-only" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "katuni4ka/tiny-random-mistral4-text-only",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use katuni4ka/tiny-random-mistral4-text-only with Docker Model Runner:
```
docker model run hf.co/katuni4ka/tiny-random-mistral4-text-only
```

katuni4ka commited on Apr 3

Commit

b680504

verified ·

1 Parent(s): d8419e7

Update configuration_mistral4.py

Browse files

Files changed (1) hide show

configuration_mistral4.py +1 -21

configuration_mistral4.py CHANGED Viewed

@@ -99,7 +99,7 @@ class Mistral4Config(PretrainedConfig):
         self.qk_rope_head_dim = qk_rope_head_dim
         self.v_head_dim = v_head_dim
         self.qk_nope_head_dim = qk_nope_head_dim
-        self.qk_head_dim = qk_nope_head_dim + qk_nope_head_dim
         self.n_group = n_group
         self.topk_group = topk_group
         self.num_experts_per_tok = num_experts_per_tok
@@ -139,25 +139,5 @@ class Mistral4Config(PretrainedConfig):
         self.attention_dropout = attention_dropout
         self.tie_word_embeddings = tie_word_embeddings
 __all__ = ["Mistral4Config"]

         self.qk_rope_head_dim = qk_rope_head_dim
         self.v_head_dim = v_head_dim
         self.qk_nope_head_dim = qk_nope_head_dim
+        self.qk_head_dim = qk_nope_head_dim + qk_rope_head_dim
         self.n_group = n_group
         self.topk_group = topk_group
         self.num_experts_per_tok = num_experts_per_tok
         self.attention_dropout = attention_dropout
         self.tie_word_embeddings = tie_word_embeddings
 __all__ = ["Mistral4Config"]