Instructions to use emonHasan01/qwen2.5-3b-text2sql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use emonHasan01/qwen2.5-3b-text2sql with Transformers:

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

pipe = pipeline("text-generation", model="emonHasan01/qwen2.5-3b-text2sql")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

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

tokenizer = AutoTokenizer.from_pretrained("emonHasan01/qwen2.5-3b-text2sql")
model = AutoModelForCausalLM.from_pretrained("emonHasan01/qwen2.5-3b-text2sql", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

PEFT
How to use emonHasan01/qwen2.5-3b-text2sql with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use emonHasan01/qwen2.5-3b-text2sql with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "emonHasan01/qwen2.5-3b-text2sql"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "emonHasan01/qwen2.5-3b-text2sql",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/emonHasan01/qwen2.5-3b-text2sql

SGLang

How to use emonHasan01/qwen2.5-3b-text2sql 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 "emonHasan01/qwen2.5-3b-text2sql" \
    --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": "emonHasan01/qwen2.5-3b-text2sql",
		"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 "emonHasan01/qwen2.5-3b-text2sql" \
        --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": "emonHasan01/qwen2.5-3b-text2sql",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use emonHasan01/qwen2.5-3b-text2sql with Docker Model Runner:
```
docker model run hf.co/emonHasan01/qwen2.5-3b-text2sql
```

emonHasan01 commited on 9 days ago

Commit

fc6ff62

verified ·

1 Parent(s): fd047ce

Text-to-SQL LoRA, exact match 7.7% -> 74.3%

Browse files

Files changed (18) hide show

.gitattributes +1 -0
README.md +165 -0
adapter_config.json +55 -0
adapter_model.safetensors +3 -0
assets/loss.png +0 -0
assets/metrics.png +0 -0
assets/per-query.png +0 -0
assets/progress.png +0 -0
chat_template.jinja +53 -0
config.json +70 -0
generation_config.json +14 -0
model-00001-of-00002.safetensors +3 -0
model-00002-of-00002.safetensors +3 -0
model.safetensors.index.json +441 -0
prompt_format.json +5 -0
results.json +294 -0
tokenizer.json +3 -0
tokenizer_config.json +195 -0

.gitattributes CHANGED Viewed

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text

 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md ADDED Viewed

	@@ -0,0 +1,165 @@

+---
+language:
+  - en
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+base_model: unsloth/Qwen2.5-3B-Instruct
+base_model_relation: finetune
+tags:
+  - text-to-sql
+  - sql
+  - unsloth
+  - lora
+  - peft
+  - qwen2.5
+  - code-generation
+datasets:
+  - b-mc2/sql-create-context
+metrics:
+  - exact_match
+model-index:
+  - name: qwen2.5-3b-text2sql
+    results:
+      - task:
+          type: text-generation
+          name: Text to SQL
+        dataset:
+          name: sql-create-context
+          type: b-mc2/sql-create-context
+          split: test
+        metrics:
+          - type: exact_match
+            name: Exact match (AST-normalised, 300 queries)
+            value: 74.3
+---
+# qwen2.5-3b-text2sql
+**Qwen2.5-3B-Instruct turned into a SQL writer.** A 16-bit LoRA fine-tune trained with
+[Unsloth](https://github.com/unslothai/unsloth) on
+[`b-mc2/sql-create-context`](https://huggingface.co/datasets/b-mc2/sql-create-context).
+Exact match rises from **7.7% to 74.3%** on 300 held-out queries,
+a gain of **+66.6 points**. Trained on a free Colab T4 in 16 minutes.
+## Results
+![Metrics](assets/metrics.png)
+| | exact match | valid SQL | token F1 | mean output chars |
+|---|---|---|---|---|
+| `unsloth/Qwen2.5-3B-Instruct` | 7.7% | 98.7% | 83.1 | 75 |
+| **this model** | **74.3%** | **100.0%** | **97.5** | **75** |
+**Exact match is computed on normalised ASTs, not strings.** Both queries are parsed with
+[`sqlglot`](https://github.com/tobymao/sqlglot) and re-emitted in a canonical form, so
+differences in case, whitespace and quoting do not count as errors.
+**The same output cleaning was applied to both models** — markdown fences stripped, leading
+prose removed, first statement taken. Without that the comparison would mostly measure
+whether a model wraps its answer in backticks. The gain above is about the SQL being right.
+![Progress](assets/progress.png)
+Measured at three points: untrained, step 150, and step 500.
+### Per-query, not just the aggregate
+![Per-query](assets/per-query.png)
+| | queries |
+|---|---|
+| both correct | 22 |
+| only this model correct | 201 |
+| only the base correct | 1 |
+| neither correct | 76 |
+### Samples
+| Question | Reference SQL | Base correct | This model correct |
+|---|---|---|---|
+| How many Attendances have a Score of 4 – 5? Question 4 | `SELECT MAX(attendance) FROM table_name_88 WHERE score = "4 –` | no | no |
+| When did woking compete? | `SELECT date FROM table_name_92 WHERE opponent = "woking"` | no | no |
+| What is the local name given to the city of Canberra? | `SELECT capital___endonym__ FROM table_1008653_1 WHERE capita` | no | yes |
+| What is the Version before 2001 with a Length of 7:42? | `SELECT version FROM table_name_64 WHERE year < 2001 AND leng` | no | yes |
+![Loss](assets/loss.png)
+## Usage
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+tok = AutoTokenizer.from_pretrained("emonHasan01/qwen2.5-3b-text2sql")
+model = AutoModelForCausalLM.from_pretrained("emonHasan01/qwen2.5-3b-text2sql", torch_dtype=torch.float16,
+                                             device_map="auto")
+schema = "CREATE TABLE head (age INTEGER, head_id VARCHAR)"
+question = "How many heads of the departments are older than 56?"
+messages = [
+    {"role": "system", "content": "You convert questions into a single SQLite query. Reply with the query only: no explanation, no markdown, no commentary."},
+    {"role": "user", "content": f"Schema:\n{schema}\n\nQuestion: {question}"},
+]
+prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tok(prompt, return_tensors="pt", add_special_tokens=False).to(model.device)
+out = model.generate(**inputs, max_new_tokens=128, do_sample=False)   # greedy
+print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
+```
+**Use greedy decoding.** Sampling introduces column and table names that are not in the
+schema. The system prompt and user template are in
+[`prompt_format.json`](prompt_format.json) and were present in every training example.
+## Training
+| | |
+|---|---|
+| Base model | `unsloth/Qwen2.5-3B-Instruct` (3.1B parameters) |
+| Method | LoRA at 16-bit via Unsloth |
+| Trainable | 29,933,568 parameters (0.97%) |
+| Rank / alpha / dropout | 16 / 16 / 0 |
+| Target modules | `q,k,v,o,gate,up,down` |
+| Learning rate | 0.0002, linear, `adamw_8bit` |
+| Batch | 2 × 4 = 8 effective |
+| Steps | 500 |
+| Max sequence | 512 tokens |
+| Peak VRAM | 6.9 GB of 15.0 |
+| Hardware | Tesla T4 |
+| Training time | ~16 minutes |
+### Why this task worked when a previous attempt did not
+An earlier experiment distilled reasoning traces into Llama-3.2-3B and GSM8K accuracy
+**fell** from 75.0% to 65.0%. That model had already been post-trained on maths by its
+authors, leaving little headroom and plenty to disturb.
+Fine-tuning pays off where the base model's output does not match the target **form**.
+Asked for SQL, an instruct model returns a markdown fence, a preamble and an explanation,
+and it reaches for columns the schema does not contain. Those are form errors, and they are
+exactly what supervised fine-tuning fixes — which is why the gain here is +66.6 points
+rather than a regression.
+This model was only published because a gate in the training notebook required at least a
++5 point improvement over the baseline before uploading.
+## Limitations
+- **Single-table, synthetic schemas.** `sql-create-context` pairs one `CREATE TABLE` with
+  one question. Multi-table joins, real production schemas, CTEs and window functions are
+  out of distribution.
+- **Exact match is strict and also too lenient.** It misses semantically equivalent queries
+  written differently, and it credits queries that parse identically but would error against
+  a real database. Nothing here was executed against actual data.
+- **Evaluated on 300 queries**, so differences under a couple of points are noise.
+- **No SQL injection or safety review.** Do not route model output straight into a database
+  without validation and a read-only connection.
+- **Greedy only.** Sampled decoding was not evaluated and hallucinates schema elements.
+- A 3B model will not match a frontier model on hard SQL.
+## Reproducing
+One Colab notebook on a Tesla T4 produced the baseline, both training stages, all four charts
+and this card. Raw numbers in [`results.json`](results.json).

adapter_config.json ADDED Viewed

	@@ -0,0 +1,55 @@

+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Qwen2ForCausalLM",
+    "parent_library": "transformers.models.qwen2.modeling_qwen2",
+    "unsloth_fixed": true
+  },
+  "base_model_name_or_path": "unsloth/Qwen2.5-3B-Instruct",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "kasa_config": null,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_bias": false,
+  "lora_dropout": 0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.21.1",
+  "qalora_group_size": 16,
+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "up_proj",
+    "gate_proj",
+    "v_proj",
+    "down_proj",
+    "o_proj",
+    "q_proj",
+    "k_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}

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assets/metrics.png ADDED Viewed

assets/per-query.png ADDED Viewed

assets/progress.png ADDED Viewed

chat_template.jinja ADDED Viewed

	@@ -0,0 +1,53 @@

+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json ADDED Viewed

	@@ -0,0 +1,70 @@

+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": null,
+  "torch_dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "max_window_layers": 70,
+  "model_type": "qwen2",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 2,
+  "pad_token_id": 151654,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000.0,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.17.0",
+  "unsloth_fixed": true,
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json ADDED Viewed

	@@ -0,0 +1,14 @@

+{
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "max_length": 32768,
+  "pad_token_id": 151654,
+  "repetition_penalty": 1.05,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "5.17.0"
+}

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+    [
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+    [
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+    [
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+      490,
+      0.04159895777702331
+    ],
+    [
+      500,
+      0.029874467849731447
+    ]
+  ],
+  "examples": [
+    {
+      "question": "How many Attendances have a Score of 4 – 5? Question 4",
+      "schema": "CREATE TABLE table_name_88 (attendance INTEGER, score VARCHAR)",
+      "gold": "SELECT MAX(attendance) FROM table_name_88 WHERE score = \"4 – 5\"",
+      "base_raw": "SELECT COUNT(attendance) FROM table_name_88 WHERE score = '4 – 5'",
+      "base": "SELECT COUNT(attendance) FROM table_name_88 WHERE score = '4 – 5'",
+      "tuned": "SELECT SUM(attendance) FROM table_name_88 WHERE score = \"4 – 5\"",
+      "base_ok": false,
+      "tuned_ok": false
+    },
+    {
+      "question": "When did woking compete?",
+      "schema": "CREATE TABLE table_name_92 (date VARCHAR, opponent VARCHAR)",
+      "gold": "SELECT date FROM table_name_92 WHERE opponent = \"woking\"",
+      "base_raw": "SELECT date FROM table_name_92 WHERE opponent = 'working'",
+      "base": "SELECT date FROM table_name_92 WHERE opponent = 'working'",
+      "tuned": "SELECT date FROM table_name_92 WHERE opponent = \"working\"",
+      "base_ok": false,
+      "tuned_ok": false
+    },
+    {
+      "question": "What is the local name given to the city of Canberra?",
+      "schema": "CREATE TABLE table_1008653_1 (capital___endonym__ VARCHAR, capital___exonym__ VARCHAR)",
+      "gold": "SELECT capital___endonym__ FROM table_1008653_1 WHERE capital___exonym__ = \"Canberra\"",
+      "base_raw": "SELECT capital___endonym__ FROM table_1008653_1 WHERE capital___exonym__ = 'Canberra'",
+      "base": "SELECT capital___endonym__ FROM table_1008653_1 WHERE capital___exonym__ = 'Canberra'",
+      "tuned": "SELECT capital___endonym__ FROM table_1008653_1 WHERE capital___exonym__ = \"Canberra\"",
+      "base_ok": false,
+      "tuned_ok": true
+    },
+    {
+      "question": "What is the Version before 2001 with a Length of 7:42?",
+      "schema": "CREATE TABLE table_name_64 (version VARCHAR, year VARCHAR, length VARCHAR)",
+      "gold": "SELECT version FROM table_name_64 WHERE year < 2001 AND length = \"7:42\"",
+      "base_raw": "SELECT version FROM table_name_64 WHERE year < '2001' AND length = '7:42' LIMIT 1",
+      "base": "SELECT version FROM table_name_64 WHERE year < '2001' AND length = '7:42' LIMIT 1",
+      "tuned": "SELECT version FROM table_name_64 WHERE year < 2001 AND length = \"7:42\"",
+      "base_ok": false,
+      "tuned_ok": true
+    }
+  ],
+  "publish_gate_min_gain": 5.0,
+  "config": {
+    "method": "LoRA 16-bit via Unsloth",
+    "r": 16,
+    "lora_alpha": 16,
+    "max_seq": 512,
+    "batch": 2,
+    "accum": 4,
+    "steps": 500,
+    "lr": 0.0002,
+    "trainable_params": 29933568,
+    "total_params": 3085938688,
+    "peak_vram_gb": 6.86,
+    "train_minutes": 15.8,
+    "hardware": "Tesla T4",
+    "seed": 3407,
+    "adapter_mb": 119.8,
+    "merged_gb": 6.18
+  }
+}

tokenizer.json ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:fde1f32229f449a02930bbc34e76cfd03d941d1bc796a35aa9e4041b7627e4a5
+size 11422334

tokenizer_config.json ADDED Viewed

	@@ -0,0 +1,195 @@

+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "is_local": false,
+  "local_files_only": false,
+  "model_max_length": 32768,
+  "pad_token": "<|vision_pad|>",
+  "padding_side": "left",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "151644": {
+      "content": "<|im_start|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "151645": {
+      "content": "<|im_end|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "151646": {
+      "content": "<|object_ref_start|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "151647": {
+      "content": "<|object_ref_end|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "151648": {
+      "content": "<|box_start|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "151649": {
+      "content": "<|box_end|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "151650": {
+      "content": "<|quad_start|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "151651": {
+      "content": "<|quad_end|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "151652": {
+      "content": "<|vision_start|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "151653": {
+      "content": "<|vision_end|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "151654": {
+      "content": "<|vision_pad|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "151655": {
+      "content": "<|image_pad|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "151656": {
+      "content": "<|video_pad|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "151657": {
+      "content": "<tool_call>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "151658": {
+      "content": "</tool_call>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "151659": {
+      "content": "<|fim_prefix|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "151660": {
+      "content": "<|fim_middle|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "151661": {
+      "content": "<|fim_suffix|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "151662": {
+      "content": "<|fim_pad|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "151663": {
+      "content": "<|repo_name|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "151664": {
+      "content": "<|file_sep|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    }
+  },
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- messages[0]['content'] }}\n    {%- else %}\n        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n    {%- endif %}\n    {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n    {%- else %}\n        {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content %}\n            {{- '\\n' + message.content }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n{\"name\": \"' }}\n            {{- tool_call.name }}\n            {{- '\", \"arguments\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- '}\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n"
+}