license: llama2
tags:
- code llama
base_model: BallisticAI/Ballistic-CodeLlama-34B-v1
inference: false
model_creator: BallisticAI
model_type: llama
prompt_template: |
### System Prompt
{system_message}
### User Message
{prompt}
### Assistant
quantized_by: BallisticAI
model-index:
- name: Ballistic-CodeLlama-34B-v1
results:
- task:
type: text-generation
dataset:
name: HumanEval
type: openai_humaneval
metrics:
- type: n/a
value: n/a
name: n/a
verified: false
CodeLlama 34B v1
- Model creator: BallisticAI
- Based on: CodeLlama 34B hf
- Merged with: CodeLlama 34B v2 && speechless-codellama-34b-v2
- Additional training with: jondurbin/airoboros-2.2
Description
This repo contains GGUF format model files for Ballistic-CodeLlama-34B-v1.
About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference.
It is also now supported by continuous batching server vLLM, allowing use of AWQ models for high-throughput concurrent inference in multi-user server scenarios. Note that, at the time of writing, overall throughput is still lower than running vLLM with unquantised models, however using AWQ enables using much smaller GPUs which can lead to easier deployment and overall cost savings. For example, a 70B model can be run on 1 x 48GB GPU instead of 2 x 80GB.
Repositories available
- GGUF model for CPU inference.
- Unquantised fp16 model in pytorch format, for GPU inference and for further conversions
How to Prompt the Model
This model accepts the Alpaca/Vicuna instruction format.
For example:
### System Prompt
You are an intelligent programming assistant.
### User Message
Implement a linked list in C++
### Assistant
...
Bias, Risks, and Limitations
This model has undergone very limited testing. Additional safety testing should be performed before any real-world deployments.
Thanks
Thanks to: