Instructions to use latimar/Phind-Codellama-34B-v2-megacode-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use latimar/Phind-Codellama-34B-v2-megacode-exl2 with Transformers:

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

pipe = pipeline("text-generation", model="latimar/Phind-Codellama-34B-v2-megacode-exl2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("latimar/Phind-Codellama-34B-v2-megacode-exl2")
model = AutoModelForCausalLM.from_pretrained("latimar/Phind-Codellama-34B-v2-megacode-exl2")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use latimar/Phind-Codellama-34B-v2-megacode-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "latimar/Phind-Codellama-34B-v2-megacode-exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "latimar/Phind-Codellama-34B-v2-megacode-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/latimar/Phind-Codellama-34B-v2-megacode-exl2

SGLang

How to use latimar/Phind-Codellama-34B-v2-megacode-exl2 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 "latimar/Phind-Codellama-34B-v2-megacode-exl2" \
    --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": "latimar/Phind-Codellama-34B-v2-megacode-exl2",
		"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 "latimar/Phind-Codellama-34B-v2-megacode-exl2" \
        --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": "latimar/Phind-Codellama-34B-v2-megacode-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use latimar/Phind-Codellama-34B-v2-megacode-exl2 with Docker Model Runner:
```
docker model run hf.co/latimar/Phind-Codellama-34B-v2-megacode-exl2
```

YAML Metadata Error:"base_model" with value "https://huggingface.co/Phind/Phind-CodeLlama-34B-v2" is not valid. Use a model id from https://hf.co/models.

Phind-CodeLlama-34B-v2 EXL2

Weights of Phind-CodeLlama-34B-v2 converted to EXL2 format.

Converted with the ExllamaV2 convert.py script, exllamav2 commit

Original model in full weights achieves 73.8 HumanEval score. Here are EXL2 quants scores:

BPW (hb=8)	HumanEval	Evol-Ins PPL	Wiki PPL	File Size (Gb)
2.55	40.24	2.0944	18.9843	10.62
2.8	63.41	2.0814	17.6326	11.58
3.0	66.46	2.0600	11.2096	12.36
4.625	70.12	2.0401	6.7243	18.63
4.8	70.73	2.0361	6.7263	19.32

Downloads

If you just do git clone you will get weights of all the quants, which is probably not what you want. You need to download (and put in the same dir) the following common files:

And the weights of a particular quant: all safetensors files + model.safetensors.index.json file from the quant directory.

Either download these files via the Web UI, or, e.g., with curl:

mkdir phind-2.55
cd phind-2.55
curl -LO https://huggingface.co/latimar/Phind-Codellama-34B-v2-megacode-exl2/raw/main/config.json
curl -LO https://huggingface.co/latimar/Phind-Codellama-34B-v2-megacode-exl2/raw/main/generation_config.json
curl -LO https://huggingface.co/latimar/Phind-Codellama-34B-v2-megacode-exl2/blob/main/special_tokens_map.json
curl -LO https://huggingface.co/latimar/Phind-Codellama-34B-v2-megacode-exl2/raw/main/tokenizer.model
curl -LO https://huggingface.co/latimar/Phind-Codellama-34B-v2-megacode-exl2/raw/main/tokenizer_config.json
curl -LO https://huggingface.co/latimar/Phind-Codellama-34B-v2-megacode-exl2/raw/main/2.55/model.safetensors.index.json
curl -LO https://huggingface.co/latimar/Phind-Codellama-34B-v2-megacode-exl2/raw/main/2.55/output-00001-of-00002.safetensors
curl -LO https://huggingface.co/latimar/Phind-Codellama-34B-v2-megacode-exl2/raw/main/2.55/output-00002-of-00002.safetensors

Datasets used for calibration and PPL measurement

Conversion

Conversion arguments:

convert.py -i ${MODEL_DIR_FP16} -o ${WIP_DIR} -cf ${MODEL_DIR_EXL} -c ${CALIBRATION_DATASET} -r 200 -mr 32 -l 4096 -ml 4096 -hb 8 -b ${BPW}

2.55 quant was converted using even more raws: -r 400 -mr 64

Perplexity

Perplexity was measured with test_inference.py script:

test_inference.py -m ${MODEL_DIR_EXL} -ed ${PPL_DATASET}

Human-Eval

Evaluation

Samples for the Human-Eval scores of EXL2 quants were generated with exl2.human-eval.py script:

python exl2.human-eval.py -m ${MODEL_DIR_EXL2} -c 4096 -o ${BPW}-samples.jsonl

Human-Eval samples of NF4/INT8 quants were generated with tf.human-eval.py script:

python tf.human-eval.py -m ${MODEL_DIR_FP16} -o nf4-samples.jsonl

Comparison

Phind says that the original model in full weights achieves 73.8 Human-Eval score. NF4 quant gives me 70.73

WizardCoder models claimed Human-Eval scores (full weights):

Model	Score
WizardCoder-Python-34B-V1.0	73.2
WizardCoder-Python-13B-V1.0	64.0

Vanilla Mistral-7B INT8 scores 27.43

EXL2 3.2-bpw quant of this model by firelzrd scores 60.97.

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