Instructions to use markfriedlander/gemma-2b-it-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use markfriedlander/gemma-2b-it-mlx with Transformers:

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

pipe = pipeline("text-generation", model="markfriedlander/gemma-2b-it-mlx")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("markfriedlander/gemma-2b-it-mlx")
model = AutoModelForCausalLM.from_pretrained("markfriedlander/gemma-2b-it-mlx")
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]:]))

MLX

How to use markfriedlander/gemma-2b-it-mlx with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("markfriedlander/gemma-2b-it-mlx")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps
LM Studio

vLLM

How to use markfriedlander/gemma-2b-it-mlx with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "markfriedlander/gemma-2b-it-mlx"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "markfriedlander/gemma-2b-it-mlx",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/markfriedlander/gemma-2b-it-mlx

SGLang

How to use markfriedlander/gemma-2b-it-mlx 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 "markfriedlander/gemma-2b-it-mlx" \
    --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": "markfriedlander/gemma-2b-it-mlx",
		"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 "markfriedlander/gemma-2b-it-mlx" \
        --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": "markfriedlander/gemma-2b-it-mlx",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

MLX LM

How to use markfriedlander/gemma-2b-it-mlx with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "markfriedlander/gemma-2b-it-mlx"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "markfriedlander/gemma-2b-it-mlx"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "markfriedlander/gemma-2b-it-mlx",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use markfriedlander/gemma-2b-it-mlx with Docker Model Runner:
```
docker model run hf.co/markfriedlander/gemma-2b-it-mlx
```

gemma-2b-it-mlx / config.json

markfriedlander

Upload 8 files

7be5206 verified 6 months ago

raw

history blame contribute delete

2.38 kB

	{
	"vocab_size": 256000,
	"max_position_embeddings": 8192,
	"hidden_size": 2048,
	"intermediate_size": 16384,
	"num_hidden_layers": 18,
	"num_attention_heads": 8,
	"head_dim": 256,
	"num_key_value_heads": 1,
	"hidden_act": "gelu",
	"initializer_range": 0.02,
	"rms_norm_eps": 1e-06,
	"use_cache": true,
	"rope_theta": 10000.0,
	"attention_bias": false,
	"attention_dropout": 0.0,
	"return_dict": true,
	"output_hidden_states": false,
	"output_attentions": false,
	"torchscript": false,
	"torch_dtype": "bfloat16",
	"use_bfloat16": false,
	"tf_legacy_loss": false,
	"pruned_heads": {},
	"tie_word_embeddings": true,
	"chunk_size_feed_forward": 0,
	"is_encoder_decoder": false,
	"is_decoder": false,
	"cross_attention_hidden_size": null,
	"add_cross_attention": false,
	"tie_encoder_decoder": false,
	"max_length": 20,
	"min_length": 0,
	"do_sample": false,
	"early_stopping": false,
	"num_beams": 1,
	"num_beam_groups": 1,
	"diversity_penalty": 0.0,
	"temperature": 1.0,
	"top_k": 50,
	"top_p": 1.0,
	"typical_p": 1.0,
	"repetition_penalty": 1.0,
	"length_penalty": 1.0,
	"no_repeat_ngram_size": 0,
	"encoder_no_repeat_ngram_size": 0,
	"bad_words_ids": null,
	"num_return_sequences": 1,
	"output_scores": false,
	"return_dict_in_generate": false,
	"forced_bos_token_id": null,
	"forced_eos_token_id": null,
	"remove_invalid_values": false,
	"exponential_decay_length_penalty": null,
	"suppress_tokens": null,
	"begin_suppress_tokens": null,
	"architectures": [
	"GemmaForCausalLM"
	],
	"finetuning_task": null,
	"id2label": {
	"0": "LABEL_0",
	"1": "LABEL_1"
	},
	"label2id": {
	"LABEL_0": 0,
	"LABEL_1": 1
	},
	"tokenizer_class": null,
	"prefix": null,
	"bos_token_id": 2,
	"pad_token_id": 0,
	"eos_token_id": 1,
	"sep_token_id": null,
	"decoder_start_token_id": null,
	"task_specific_params": null,
	"problem_type": null,
	"_name_or_path": "/Users/prince_canuma/.cache/huggingface/hub/models--google--gemma-2b-it/snapshots/9642e777f24fde593d204a9b2471dce33334e64a",
	"transformers_version": "4.38.0",
	"model_type": "gemma",
	"rope_scaling": null,
	"quantization": {
	"group_size": 64,
	"bits": 4
	}
	}