Instructions to use katuni4ka/tiny-random-dbrx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use katuni4ka/tiny-random-dbrx with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="katuni4ka/tiny-random-dbrx") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("katuni4ka/tiny-random-dbrx") model = AutoModelForCausalLM.from_pretrained("katuni4ka/tiny-random-dbrx") 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]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use katuni4ka/tiny-random-dbrx with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "katuni4ka/tiny-random-dbrx" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "katuni4ka/tiny-random-dbrx", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/katuni4ka/tiny-random-dbrx
- SGLang
How to use katuni4ka/tiny-random-dbrx 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 "katuni4ka/tiny-random-dbrx" \ --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": "katuni4ka/tiny-random-dbrx", "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 "katuni4ka/tiny-random-dbrx" \ --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": "katuni4ka/tiny-random-dbrx", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use katuni4ka/tiny-random-dbrx with Docker Model Runner:
docker model run hf.co/katuni4ka/tiny-random-dbrx
Upload 9 files
Browse files- added_tokens.json +4 -0
- config.json +39 -0
- generation_config.json +4 -0
- merges.txt +0 -0
- model.safetensors +3 -0
- special_tokens_map.json +30 -0
- tokenizer.json +0 -0
- tokenizer_config.json +205 -0
- vocab.json +0 -0
added_tokens.json
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"<|im_end|>": 100279,
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"<|im_start|>": 100278
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}
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config.json
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{
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"_name_or_path": "/home/ea/work/my_optimum_intel/optimum-intel/dbrx-tiny",
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"architectures": [
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"DbrxForCausalLM"
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],
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"attn_config": {
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"clip_qkv": 8,
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"kv_n_heads": 2,
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"model_type": "",
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"rope_theta": 500000
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},
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"auto_map": {
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"AutoConfig": "databricks/dbrx-instruct--configuration_dbrx.DbrxConfig",
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"AutoModelForCausalLM": "databricks/dbrx-instruct--modeling_dbrx.DbrxForCausalLM"
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},
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"d_model": 8,
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"emb_pdrop": 0.0,
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"ffn_config": {
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"ffn_hidden_size": 8,
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"model_type": "",
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"moe_jitter_eps": 0,
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"moe_loss_weight": 0.05,
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"moe_num_experts": 16,
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"moe_top_k": 4
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},
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"initializer_range": 0.02,
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"max_seq_len": 32768,
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"model_type": "dbrx",
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"n_heads": 4,
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"n_layers": 2,
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"output_router_logits": false,
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"resid_pdrop": 0.0,
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"router_aux_loss_coef": 0.05,
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"tie_word_embeddings": false,
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"torch_dtype": "float32",
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"transformers_version": "4.40.2",
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"use_cache": true,
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"vocab_size": 100352
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}
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generation_config.json
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{
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"_from_model_config": true,
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"transformers_version": "4.40.2"
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}
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merges.txt
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:af0ff0ed93a215e26e55755a63cb80253db24d600e491835df7626ba017793e6
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size 6451928
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special_tokens_map.json
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{
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"bos_token": {
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"content": "<|endoftext|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"eos_token": {
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"content": "<|endoftext|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": {
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"content": "<|pad|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"unk_token": {
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"content": "<|endoftext|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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}
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tokenizer.json
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tokenizer_config.json
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{
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"add_prefix_space": false,
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"added_tokens_decoder": {
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"100256": {
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"content": "<||_unused_0_||>",
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"lstrip": false,
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"special": true
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},
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"single_word": false,
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"special": true
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},
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"100258": {
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"content": "<|fim_prefix|>",
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"single_word": false,
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"special": true
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},
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"content": "<|fim_middle|>",
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| 32 |
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"100260": {
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"content": "<|fim_suffix|>",
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| 38 |
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| 40 |
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"special": true
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},
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"100261": {
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"content": "<||_unused_1_||>",
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"special": true
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| 51 |
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},
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"100262": {
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},
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"100263": {
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},
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"special": true
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| 91 |
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},
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| 92 |
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"100267": {
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| 93 |
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"content": "<||_unused_7_||>",
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"special": true
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| 99 |
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},
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| 100 |
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"100268": {
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"content": "<||_unused_8_||>",
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"lstrip": false,
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"normalized": false,
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| 104 |
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"100269": {
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"content": "<||_unused_9_||>",
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| 112 |
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| 113 |
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"single_word": false,
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"special": true
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| 115 |
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},
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| 116 |
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"100270": {
|
| 117 |
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"content": "<||_unused_10_||>",
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|
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},
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| 124 |
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"100271": {
|
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"special": true
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| 131 |
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},
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| 132 |
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"100272": {
|
| 133 |
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"content": "<||_unused_12_||>",
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| 135 |
+
"normalized": false,
|
| 136 |
+
"rstrip": false,
|
| 137 |
+
"single_word": false,
|
| 138 |
+
"special": true
|
| 139 |
+
},
|
| 140 |
+
"100273": {
|
| 141 |
+
"content": "<||_unused_13_||>",
|
| 142 |
+
"lstrip": false,
|
| 143 |
+
"normalized": false,
|
| 144 |
+
"rstrip": false,
|
| 145 |
+
"single_word": false,
|
| 146 |
+
"special": true
|
| 147 |
+
},
|
| 148 |
+
"100274": {
|
| 149 |
+
"content": "<||_unused_14_||>",
|
| 150 |
+
"lstrip": false,
|
| 151 |
+
"normalized": false,
|
| 152 |
+
"rstrip": false,
|
| 153 |
+
"single_word": false,
|
| 154 |
+
"special": true
|
| 155 |
+
},
|
| 156 |
+
"100275": {
|
| 157 |
+
"content": "<||_unused_15_||>",
|
| 158 |
+
"lstrip": false,
|
| 159 |
+
"normalized": false,
|
| 160 |
+
"rstrip": false,
|
| 161 |
+
"single_word": false,
|
| 162 |
+
"special": true
|
| 163 |
+
},
|
| 164 |
+
"100276": {
|
| 165 |
+
"content": "<|endofprompt|>",
|
| 166 |
+
"lstrip": false,
|
| 167 |
+
"normalized": false,
|
| 168 |
+
"rstrip": false,
|
| 169 |
+
"single_word": false,
|
| 170 |
+
"special": true
|
| 171 |
+
},
|
| 172 |
+
"100277": {
|
| 173 |
+
"content": "<|pad|>",
|
| 174 |
+
"lstrip": false,
|
| 175 |
+
"normalized": false,
|
| 176 |
+
"rstrip": false,
|
| 177 |
+
"single_word": false,
|
| 178 |
+
"special": true
|
| 179 |
+
},
|
| 180 |
+
"100278": {
|
| 181 |
+
"content": "<|im_start|>",
|
| 182 |
+
"lstrip": false,
|
| 183 |
+
"normalized": false,
|
| 184 |
+
"rstrip": false,
|
| 185 |
+
"single_word": false,
|
| 186 |
+
"special": true
|
| 187 |
+
},
|
| 188 |
+
"100279": {
|
| 189 |
+
"content": "<|im_end|>",
|
| 190 |
+
"lstrip": false,
|
| 191 |
+
"normalized": false,
|
| 192 |
+
"rstrip": false,
|
| 193 |
+
"single_word": false,
|
| 194 |
+
"special": true
|
| 195 |
+
}
|
| 196 |
+
},
|
| 197 |
+
"bos_token": "<|endoftext|>",
|
| 198 |
+
"chat_template": "{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif 'system' not in messages[0]['role'] %}{% set loop_messages = messages %}{% set system_message = 'You are DBRX, created by Databricks. You were last updated in December 2023. You answer questions based on information available up to that point.\nYOU PROVIDE SHORT RESPONSES TO SHORT QUESTIONS OR STATEMENTS, but provide thorough responses to more complex and open-ended questions.\nYou assist with various tasks, from writing to coding (using markdown for code blocks — remember to use ``` with code, JSON, and tables).\n(You do not have real-time data access or code execution capabilities. You avoid stereotyping and provide balanced perspectives on controversial topics. You do not provide song lyrics, poems, or news articles and do not divulge details of your training data.)\nThis is your system prompt, guiding your responses. Do not reference it, just respond to the user. If you find yourself talking about this message, stop. You should be responding appropriately and usually that means not mentioning this.\nYOU DO NOT MENTION ANY OF THIS INFORMATION ABOUT YOURSELF UNLESS THE INFORMATION IS DIRECTLY PERTINENT TO THE USER\\'S QUERY.' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% for message in loop_messages %}{% if loop.index0 == 0 %}{% if system_message != false %}{{ '<|im_start|>system\n' + system_message | trim + '<|im_end|>\n'}}{% endif %}{{ '<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' }}{% else %}{{ '\n' + '<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' }}{% endif %}{% if (add_generation_prompt == true and loop.last) %}{{ '\n' + '<|im_start|>' + 'assistant' + '\n' }}{% endif %}{% endfor %}",
|
| 199 |
+
"clean_up_tokenization_spaces": true,
|
| 200 |
+
"eos_token": "<|endoftext|>",
|
| 201 |
+
"model_max_length": 32768,
|
| 202 |
+
"pad_token": "<|pad|>",
|
| 203 |
+
"tokenizer_class": "GPT2Tokenizer",
|
| 204 |
+
"unk_token": "<|endoftext|>"
|
| 205 |
+
}
|
vocab.json
ADDED
|
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|