Text Generation
Transformers
Safetensors
gemma2
mergekit
Merge
conversational
text-generation-inference
Instructions to use Lil-R/2_PRYMMAL-ECE-2B-SLERP-V2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Lil-R/2_PRYMMAL-ECE-2B-SLERP-V2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Lil-R/2_PRYMMAL-ECE-2B-SLERP-V2") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Lil-R/2_PRYMMAL-ECE-2B-SLERP-V2") model = AutoModelForCausalLM.from_pretrained("Lil-R/2_PRYMMAL-ECE-2B-SLERP-V2") 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 Lil-R/2_PRYMMAL-ECE-2B-SLERP-V2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Lil-R/2_PRYMMAL-ECE-2B-SLERP-V2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Lil-R/2_PRYMMAL-ECE-2B-SLERP-V2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Lil-R/2_PRYMMAL-ECE-2B-SLERP-V2
- SGLang
How to use Lil-R/2_PRYMMAL-ECE-2B-SLERP-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 "Lil-R/2_PRYMMAL-ECE-2B-SLERP-V2" \ --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": "Lil-R/2_PRYMMAL-ECE-2B-SLERP-V2", "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 "Lil-R/2_PRYMMAL-ECE-2B-SLERP-V2" \ --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": "Lil-R/2_PRYMMAL-ECE-2B-SLERP-V2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Lil-R/2_PRYMMAL-ECE-2B-SLERP-V2 with Docker Model Runner:
docker model run hf.co/Lil-R/2_PRYMMAL-ECE-2B-SLERP-V2
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base_model:
- google/gemma-2-2b-it
- zake7749/gemma-2-2b-it-chinese-kyara-dpo
library_name: transformers
tags:
- mergekit
- merge
license: apache-2.0
---
# merged_model
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [google/gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it)
* [zake7749/gemma-2-2b-it-chinese-kyara-dpo](https://huggingface.co/zake7749/gemma-2-2b-it-chinese-kyara-dpo)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: zake7749/gemma-2-2b-it-chinese-kyara-dpo
layer_range: [0, 26]
- model: google/gemma-2-2b-it
layer_range: [0, 26]
merge_method: slerp
base_model: zake7749/gemma-2-2b-it-chinese-kyara-dpo
parameters:
t:
- filter: self_attn
value: [0, 0.25, 0.5, 0.75, 1]
- filter: mlp
value: [1, 0.75, 0.5, 0.25, 0]
- value: 0.5
dtype: bfloat16
``` |