Instructions to use Finisha-F-scratch/Lamina-large-2b-pretrain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Finisha-F-scratch/Lamina-large-2b-pretrain with Transformers:

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

pipe = pipeline("text-generation", model="Finisha-F-scratch/Lamina-large-2b-pretrain")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Finisha-F-scratch/Lamina-large-2b-pretrain")
model = AutoModelForCausalLM.from_pretrained("Finisha-F-scratch/Lamina-large-2b-pretrain", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Finisha-F-scratch/Lamina-large-2b-pretrain with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Finisha-F-scratch/Lamina-large-2b-pretrain"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Finisha-F-scratch/Lamina-large-2b-pretrain",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Finisha-F-scratch/Lamina-large-2b-pretrain

SGLang

How to use Finisha-F-scratch/Lamina-large-2b-pretrain 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 "Finisha-F-scratch/Lamina-large-2b-pretrain" \
    --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": "Finisha-F-scratch/Lamina-large-2b-pretrain",
		"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 "Finisha-F-scratch/Lamina-large-2b-pretrain" \
        --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": "Finisha-F-scratch/Lamina-large-2b-pretrain",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Finisha-F-scratch/Lamina-large-2b-pretrain with Docker Model Runner:
```
docker model run hf.co/Finisha-F-scratch/Lamina-large-2b-pretrain
```

Clemylia commited on Nov 19, 2025

Commit

0e03638

verified ·

1 Parent(s): cde84d1

Delete chat_template.jinja

Browse files

Files changed (1) hide show

chat_template.jinja +0 -13

chat_template.jinja DELETED Viewed

@@ -1,13 +0,0 @@
-{% for message in messages %}
-  {% if message['role'] == 'user' %}
-    {# Début du bloc de conversation et rôle utilisateur #}
-    {{ '<startoftext>SOURCE: ' + message['content'] + ' TEXTE:' }}
-  {% elif message['role'] == 'assistant' %}
-    {# Réponse de l'assistant #}
-    {{ ' ' + message['content'] + '<endoftext>' }}
-  {% endif %}
-{% endfor %}
-{# Ajout d'une balise de fin de conversation si le dernier message était la réponse du modèle #}
-{%- if messages[-1]['role'] != 'assistant' and add_generation_prompt is not defined or add_generation_prompt == true -%}
-{{- '<endoftext>' -}}
-{%- endif -%}