Instructions to use MultiverseComputingCAI/LittleLamb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MultiverseComputingCAI/LittleLamb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MultiverseComputingCAI/LittleLamb") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("MultiverseComputingCAI/LittleLamb") model = AutoModelForCausalLM.from_pretrained("MultiverseComputingCAI/LittleLamb") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.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=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use MultiverseComputingCAI/LittleLamb with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MultiverseComputingCAI/LittleLamb" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MultiverseComputingCAI/LittleLamb", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/MultiverseComputingCAI/LittleLamb
- SGLang
How to use MultiverseComputingCAI/LittleLamb 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 "MultiverseComputingCAI/LittleLamb" \ --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": "MultiverseComputingCAI/LittleLamb", "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 "MultiverseComputingCAI/LittleLamb" \ --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": "MultiverseComputingCAI/LittleLamb", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use MultiverseComputingCAI/LittleLamb with Docker Model Runner:
docker model run hf.co/MultiverseComputingCAI/LittleLamb
Upload README.md with huggingface_hub
Browse files
README.md
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### Quantitative Results (Inference Performance)
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#### Metrics reported
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- **System Output Throughput**: Mean output tokens per second across all concurrent requests over the benchmarking phase.
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- **End-to-End Latency per Query:** Median end-to-end response time for each query from the time the query is sent.
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- **Output Speed per Query:** Median output tokens per second after the first token is received for each query.
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- **Time to first token (TTFT):** Median
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- **Estimated Peak Memory Usage:** KV cache utilization is monitored during the phase and we estimate memory usage as follows: $model\_ weights_{gb} + kv\_ cache_{usage\_pct} × (nvml\_used_{gb} − model\_ weights_{gb})$
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- **Model weights:**
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#### Performance evaluation conditions
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- **Streaming**: Benchmarking is conducted with streaming enabled.
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**Summary of improvements:**
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### Quantitative Results (Inference Performance)
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#### Metrics reported
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- **System Output Throughput (higher is better)**: Mean output tokens per second across all concurrent requests over the benchmarking phase.
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- **End-to-End Latency per Query (lower is better):** Median end-to-end response time for each query from the time the query is sent.
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- **Output Speed per Query (higher is better):** Median output tokens per second after the first token is received for each query.
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- **Time to first token (TTFT) (lower is better):** Median
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- **Estimated Peak Memory Usage (lower is better):** KV cache utilization is monitored during the phase and we estimate memory usage as follows: $model\_ weights_{gb} + kv\_ cache_{usage\_pct} × (nvml\_used_{gb} − model\_ weights_{gb})$
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- **Model weights (lower is better):**
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**Summary of improvements:** LittleLamb shows a slight improvement in performance with respect to the original Qwen Model. This is expected as for such small models, VRAM usage is dominated by KV cache and not model weights.
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#### Performance evaluation conditions
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- **Streaming**: Benchmarking is conducted with streaming enabled.
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**Summary of improvements:** LittleLamb shows a slight improvement in performance with respect to the original Qwen Model. This is expected as for such small models, VRAM usage is dominated by KV cache and not model weights.
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