Instructions to use hmankar01/gridllm-analyst with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hmankar01/gridllm-analyst with Transformers:

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

pipe = pipeline("text-generation", model="hmankar01/gridllm-analyst")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hmankar01/gridllm-analyst")
model = AutoModelForCausalLM.from_pretrained("hmankar01/gridllm-analyst", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use hmankar01/gridllm-analyst with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hmankar01/gridllm-analyst"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hmankar01/gridllm-analyst",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/hmankar01/gridllm-analyst

SGLang

How to use hmankar01/gridllm-analyst 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 "hmankar01/gridllm-analyst" \
    --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": "hmankar01/gridllm-analyst",
		"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 "hmankar01/gridllm-analyst" \
        --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": "hmankar01/gridllm-analyst",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use hmankar01/gridllm-analyst with Docker Model Runner:
```
docker model run hf.co/hmankar01/gridllm-analyst
```

hmankar01 commited on Feb 11

Commit

f75a636

verified ·

1 Parent(s): dca2faf

Update config.json

Browse files

Files changed (1) hide show

config.json +46 -33

config.json CHANGED Viewed

@@ -1,35 +1,48 @@
 {
-    "architectures": ["LlamaForCausalLM"],
-    "attention_bias": False,
-    "attention_dropout": 0.0,
-    "bos_token_id": 128000,
-    "dtype": "float16",
-    "eos_token_id": 128001,
-    "head_dim": 128,
-    "hidden_act": "silu",
-    "hidden_size": 3072,
-    "initializer_range": 0.02,
-    "intermediate_size": 8192,
-    "max_position_embeddings": 131072,
-    "mlp_bias": False,
-    "model_type": "llama",
-    "num_attention_heads": 24,
-    "num_hidden_layers": 28,
-    "num_key_value_heads": 8,
-    "pad_token_id": 128004,
-    "pretraining_tp": 1,
-    "rms_norm_eps": 1e-05,
-    "rope_scaling": {
-        "factor": 32.0,
-        "high_freq_factor": 4.0,
-        "low_freq_factor": 1.0,
-        "original_max_position_embeddings": 8192,
-        "rope_type": "llama3"
-    },
-    "rope_theta": 500000.0,
-    "tie_word_embeddings": True,
-    "transformers_version": "4.56.2",
-    "unsloth_fixed": True,
-    "use_cache": True,
-    "vocab_size": 128256
 }

 {
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "dtype": "float16",
+  "eos_token_id": 128001,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 3072,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 24,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "pad_token_id": 128004,
+  "pretraining_tp": 1,
+  "quantization_config": {
+    "bnb_4bit_compute_dtype": "float16",
+    "bnb_4bit_quant_type": "nf4",
+    "bnb_4bit_use_double_quant": true,
+    "load_in_4bit": true,
+    "llm_int8_enable_fp32_cpu_offload": false,
+    "llm_int8_has_fp16_weight": false,
+    "llm_int8_threshold": 6.0,
+    "bnb_4bit_use_nested_quant": false
+}
+  },
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 32.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": true,
+  "transformers_version": "4.56.2",
+  "unsloth_fixed": true,
+  "use_cache": true,
+  "vocab_size": 128256
 }