Instructions to use T3lli/test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use T3lli/test with Transformers:

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

pipe = pipeline("text-generation", model="T3lli/test")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("T3lli/test", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use T3lli/test with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "T3lli/test"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "T3lli/test",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/T3lli/test

SGLang

How to use T3lli/test 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 "T3lli/test" \
    --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": "T3lli/test",
		"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 "T3lli/test" \
        --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": "T3lli/test",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use T3lli/test with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for T3lli/test to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for T3lli/test to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for T3lli/test to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="T3lli/test",
    max_seq_length=2048,
)

Docker Model Runner
How to use T3lli/test with Docker Model Runner:
```
docker model run hf.co/T3lli/test
```

T3lli commited on Dec 9, 2024

Commit

8f66424

verified ·

1 Parent(s): 8e539d9

Upload model trained with Unsloth

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Files changed (2) hide show

README.md +7 -8
adapter_config.json +5 -5

README.md CHANGED Viewed

@@ -1,16 +1,15 @@
 ---
 base_model: unsloth/llama-3.2-1b-instruct
 tags:
-  - text-generation-inference
-  - text-generation
-  - transformers
-  - unsloth
-  - llama
-  - trl
 license: apache-2.0
 language:
-  - en
 ---
 # Model Card for Model ID

 ---
 base_model: unsloth/llama-3.2-1b-instruct
 tags:
+- text-generation-inference
+- text-generation
+- transformers
+- unsloth
+- llama
+- trl
 license: apache-2.0
 language:
+- en
 ---
 # Model Card for Model ID

adapter_config.json CHANGED Viewed

@@ -20,13 +20,13 @@
   "rank_pattern": {},
   "revision": null,
   "target_modules": [
-    "o_proj",
-    "down_proj",
-    "v_proj",
     "gate_proj",
-    "q_proj",
     "up_proj",
-    "k_proj"
   ],
   "task_type": "CAUSAL_LM",
   "use_dora": false,

   "rank_pattern": {},
   "revision": null,
   "target_modules": [
     "gate_proj",
+    "k_proj",
+    "down_proj",
+    "o_proj",
     "up_proj",
+    "v_proj",
+    "q_proj"
   ],
   "task_type": "CAUSAL_LM",
   "use_dora": false,