Instructions to use vopash/KwaiCoder-DS-V2-Lite-Base-mlx-4Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vopash/KwaiCoder-DS-V2-Lite-Base-mlx-4Bit with Transformers:

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

pipe = pipeline("text-generation", model="vopash/KwaiCoder-DS-V2-Lite-Base-mlx-4Bit", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("vopash/KwaiCoder-DS-V2-Lite-Base-mlx-4Bit", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("vopash/KwaiCoder-DS-V2-Lite-Base-mlx-4Bit", trust_remote_code=True, device_map="auto")
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 vopash/KwaiCoder-DS-V2-Lite-Base-mlx-4Bit 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("vopash/KwaiCoder-DS-V2-Lite-Base-mlx-4Bit")

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 Settings
LM Studio

vLLM

How to use vopash/KwaiCoder-DS-V2-Lite-Base-mlx-4Bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vopash/KwaiCoder-DS-V2-Lite-Base-mlx-4Bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vopash/KwaiCoder-DS-V2-Lite-Base-mlx-4Bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/vopash/KwaiCoder-DS-V2-Lite-Base-mlx-4Bit

SGLang

How to use vopash/KwaiCoder-DS-V2-Lite-Base-mlx-4Bit 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 "vopash/KwaiCoder-DS-V2-Lite-Base-mlx-4Bit" \
    --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": "vopash/KwaiCoder-DS-V2-Lite-Base-mlx-4Bit",
		"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 "vopash/KwaiCoder-DS-V2-Lite-Base-mlx-4Bit" \
        --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": "vopash/KwaiCoder-DS-V2-Lite-Base-mlx-4Bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

MLX LM

How to use vopash/KwaiCoder-DS-V2-Lite-Base-mlx-4Bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "vopash/KwaiCoder-DS-V2-Lite-Base-mlx-4Bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "vopash/KwaiCoder-DS-V2-Lite-Base-mlx-4Bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "vopash/KwaiCoder-DS-V2-Lite-Base-mlx-4Bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use vopash/KwaiCoder-DS-V2-Lite-Base-mlx-4Bit with Docker Model Runner:
```
docker model run hf.co/vopash/KwaiCoder-DS-V2-Lite-Base-mlx-4Bit
```
Atomic Chat

vopash commited on Feb 5

Commit

39281d9

verified ·

1 Parent(s): b175f9f

Upload config.json with huggingface_hub

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config.json +69 -0

config.json ADDED Viewed

	@@ -0,0 +1,69 @@

+{
+    "architectures": [
+        "DeepseekV2ForCausalLM"
+    ],
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "auto_map": {
+        "AutoConfig": "configuration_deepseek.DeepseekV2Config",
+        "AutoModel": "modeling_deepseek.DeepseekV2Model",
+        "AutoModelForCausalLM": "modeling_deepseek.DeepseekV2ForCausalLM"
+    },
+    "aux_loss_alpha": 0.001,
+    "bos_token_id": 100000,
+    "eos_token_id": 100001,
+    "first_k_dense_replace": 1,
+    "hidden_act": "silu",
+    "hidden_size": 2048,
+    "initializer_range": 0.02,
+    "intermediate_size": 10944,
+    "kv_lora_rank": 512,
+    "max_position_embeddings": 163840,
+    "model_type": "deepseek_v2",
+    "moe_intermediate_size": 1408,
+    "moe_layer_freq": 1,
+    "n_group": 1,
+    "n_routed_experts": 64,
+    "n_shared_experts": 2,
+    "norm_topk_prob": false,
+    "num_attention_heads": 16,
+    "num_experts_per_tok": 6,
+    "num_hidden_layers": 27,
+    "num_key_value_heads": 16,
+    "pretraining_tp": 1,
+    "q_lora_rank": null,
+    "qk_nope_head_dim": 128,
+    "qk_rope_head_dim": 64,
+    "quantization": {
+        "group_size": 64,
+        "bits": 4,
+        "mode": "affine"
+    },
+    "quantization_config": {
+        "group_size": 64,
+        "bits": 4,
+        "mode": "affine"
+    },
+    "rms_norm_eps": 1e-06,
+    "rope_scaling": {
+        "beta_fast": 32,
+        "beta_slow": 1,
+        "factor": 40,
+        "mscale": 0.707,
+        "mscale_all_dim": 0.707,
+        "original_max_position_embeddings": 4096,
+        "type": "yarn"
+    },
+    "rope_theta": 10000,
+    "routed_scaling_factor": 1.0,
+    "scoring_func": "softmax",
+    "seq_aux": true,
+    "tie_word_embeddings": false,
+    "topk_group": 1,
+    "topk_method": "greedy",
+    "torch_dtype": "bfloat16",
+    "transformers_version": "4.39.3",
+    "use_cache": true,
+    "v_head_dim": 128,
+    "vocab_size": 102400
+}