Instructions to use TechxGenus/Mini-Jamba-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TechxGenus/Mini-Jamba-v2 with Transformers:

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

pipe = pipeline("text-generation", model="TechxGenus/Mini-Jamba-v2", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TechxGenus/Mini-Jamba-v2", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("TechxGenus/Mini-Jamba-v2", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TechxGenus/Mini-Jamba-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TechxGenus/Mini-Jamba-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TechxGenus/Mini-Jamba-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/TechxGenus/Mini-Jamba-v2

SGLang

How to use TechxGenus/Mini-Jamba-v2 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 "TechxGenus/Mini-Jamba-v2" \
    --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": "TechxGenus/Mini-Jamba-v2",
		"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 "TechxGenus/Mini-Jamba-v2" \
        --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": "TechxGenus/Mini-Jamba-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TechxGenus/Mini-Jamba-v2 with Docker Model Runner:
```
docker model run hf.co/TechxGenus/Mini-Jamba-v2
```

TechxGenus commited on Mar 31, 2024

Commit

35b1d48

verified ·

1 Parent(s): 7f137f4

Upload README.md

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README.md +3 -3

README.md CHANGED Viewed

@@ -7,7 +7,7 @@ tags:
 - moe
 ---
-### Mini-Jamba
 [**Experimental Version**] We initialized the model according to [Jamba](https://huggingface.co/ai21labs/Jamba-v0.1), but with much smaller parameters. It was then trained using about 1B of python code, and has the simplest python code generation capabilities.
@@ -31,12 +31,12 @@ prompt = '''def min(arr):
 '''
 tokenizer = AutoTokenizer.from_pretrained(
-    "TechxGenus/Mini-Jamba",
     trust_remote_code=True,
 )
 tokenizer.pad_token = tokenizer.eos_token
 model = AutoModelForCausalLM.from_pretrained(
-    "TechxGenus/Mini-Jamba",
     torch_dtype=torch.float16,
     device_map="auto",
     trust_remote_code=True,

 - moe
 ---
+### Mini-Jamba-v2
 [**Experimental Version**] We initialized the model according to [Jamba](https://huggingface.co/ai21labs/Jamba-v0.1), but with much smaller parameters. It was then trained using about 1B of python code, and has the simplest python code generation capabilities.
 '''
 tokenizer = AutoTokenizer.from_pretrained(
+    "TechxGenus/Mini-Jamba-v2",
     trust_remote_code=True,
 )
 tokenizer.pad_token = tokenizer.eos_token
 model = AutoModelForCausalLM.from_pretrained(
+    "TechxGenus/Mini-Jamba-v2",
     torch_dtype=torch.float16,
     device_map="auto",
     trust_remote_code=True,