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README.md

@@ -1,162 +1,162 @@
----
-license: llama3.1
-model-index:
-- name: Llama-3.1-8B-Lexi-Uncensored-V2
-  results:
-  - task:
-      type: text-generation
-      name: Text Generation
-    dataset:
-      name: IFEval (0-Shot)
-      type: HuggingFaceH4/ifeval
-      args:
-        num_few_shot: 0
-    metrics:
-    - type: inst_level_strict_acc and prompt_level_strict_acc
-      value: 77.92
-      name: strict accuracy
-    source:
-      url: >-
-        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2
-      name: Open LLM Leaderboard
-  - task:
-      type: text-generation
-      name: Text Generation
-    dataset:
-      name: BBH (3-Shot)
-      type: BBH
-      args:
-        num_few_shot: 3
-    metrics:
-    - type: acc_norm
-      value: 29.69
-      name: normalized accuracy
-    source:
-      url: >-
-        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2
-      name: Open LLM Leaderboard
-  - task:
-      type: text-generation
-      name: Text Generation
-    dataset:
-      name: MATH Lvl 5 (4-Shot)
-      type: hendrycks/competition_math
-      args:
-        num_few_shot: 4
-    metrics:
-    - type: exact_match
-      value: 16.92
-      name: exact match
-    source:
-      url: >-
-        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2
-      name: Open LLM Leaderboard
-  - task:
-      type: text-generation
-      name: Text Generation
-    dataset:
-      name: GPQA (0-shot)
-      type: Idavidrein/gpqa
-      args:
-        num_few_shot: 0
-    metrics:
-    - type: acc_norm
-      value: 4.36
-      name: acc_norm
-    source:
-      url: >-
-        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2
-      name: Open LLM Leaderboard
-  - task:
-      type: text-generation
-      name: Text Generation
-    dataset:
-      name: MuSR (0-shot)
-      type: TAUR-Lab/MuSR
-      args:
-        num_few_shot: 0
-    metrics:
-    - type: acc_norm
-      value: 7.77
-      name: acc_norm
-    source:
-      url: >-
-        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2
-      name: Open LLM Leaderboard
-  - task:
-      type: text-generation
-      name: Text Generation
-    dataset:
-      name: MMLU-PRO (5-shot)
-      type: TIGER-Lab/MMLU-Pro
-      config: main
-      split: test
-      args:
-        num_few_shot: 5
-    metrics:
-    - type: acc
-      value: 30.9
-      name: accuracy
-    source:
-      url: >-
-        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2
-      name: Open LLM Leaderboard
-library_name: transformers
-base_model:
-- Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2
----
-
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/644ad182f434a6a63b18eee6/7mnEJyioRzQaWz8xLM4KI.png)
-
-VERSION 2 Update Notes:
----
-- More compliant
-- Smarter
-- For best response, use this system prompt (feel free to expand upon it as you wish):
-
-Think step by step with a logical reasoning and intellectual sense before you provide any response.
-
-- For more uncensored and compliant response, you can expand the system message differently, or simply enter a dot "." as system message.
-
-- IMPORTANT: Upon further investigation, the Q4 seems to have refusal issues sometimes. 
-There seems to be some of the fine-tune loss happening due to the quantization. I will look into it for V3. 
-Until then, I suggest you run F16 or Q8 if possible.
-
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/644ad182f434a6a63b18eee6/zaHhRjsk3rvo_YewgXV2Z.png)
-
-GENERAL INFO:
----
-
-This model is based on Llama-3.1-8b-Instruct, and is governed by [META LLAMA 3.1 COMMUNITY LICENSE AGREEMENT](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE)
-
-Lexi is uncensored, which makes the model compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. 
-
-You are responsible for any content you create using this model. Please use it responsibly.
-
-Lexi is licensed according to Meta's Llama license. I grant permission for any use, including commercial, that falls within accordance with Meta's Llama-3.1 license.
-
-IMPORTANT:
----
-Use the same template as the official Llama 3.1 8B instruct.
-System tokens must be present during inference, even if you set an empty system message. If you are unsure, just add a short system message as you wish.
-
-FEEDBACK:
----
-If you find any issues or have suggestions for improvements, feel free to leave a review and I will look into it for upcoming improvements and next version.
-
-
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/644ad182f434a6a63b18eee6/uqJv-R1LeJEfMxi1nmTH5.png)
-
-
-# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
-Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Orenguteng__Llama-3.1-8B-Lexi-Uncensored-V2)
-
-|      Metric       |Value|
-|-------------------|----:|
-|Avg.               |27.93|
-|IFEval (0-Shot)    |77.92|
-|BBH (3-Shot)       |29.69|
-|MATH Lvl 5 (4-Shot)|16.92|
-|GPQA (0-shot)      | 4.36|
-|MuSR (0-shot)      | 7.77|
+---
+license: llama3.1
+model-index:
+- name: Llama-3.1-8B-Lexi-Uncensored-V2
+  results:
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: IFEval (0-Shot)
+      type: HuggingFaceH4/ifeval
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: inst_level_strict_acc and prompt_level_strict_acc
+      value: 77.92
+      name: strict accuracy
+    source:
+      url: >-
+        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: BBH (3-Shot)
+      type: BBH
+      args:
+        num_few_shot: 3
+    metrics:
+    - type: acc_norm
+      value: 29.69
+      name: normalized accuracy
+    source:
+      url: >-
+        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MATH Lvl 5 (4-Shot)
+      type: hendrycks/competition_math
+      args:
+        num_few_shot: 4
+    metrics:
+    - type: exact_match
+      value: 16.92
+      name: exact match
+    source:
+      url: >-
+        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: GPQA (0-shot)
+      type: Idavidrein/gpqa
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: acc_norm
+      value: 4.36
+      name: acc_norm
+    source:
+      url: >-
+        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MuSR (0-shot)
+      type: TAUR-Lab/MuSR
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: acc_norm
+      value: 7.77
+      name: acc_norm
+    source:
+      url: >-
+        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MMLU-PRO (5-shot)
+      type: TIGER-Lab/MMLU-Pro
+      config: main
+      split: test
+      args:
+        num_few_shot: 5
+    metrics:
+    - type: acc
+      value: 30.9
+      name: accuracy
+    source:
+      url: >-
+        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2
+      name: Open LLM Leaderboard
+library_name: transformers
+base_model:
+- Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2
+---
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/644ad182f434a6a63b18eee6/7mnEJyioRzQaWz8xLM4KI.png)
+
+VERSION 2 Update Notes:
+---
+- More compliant
+- Smarter
+- For best response, use this system prompt (feel free to expand upon it as you wish):
+
+Think step by step with a logical reasoning and intellectual sense before you provide any response.
+
+- For more uncensored and compliant response, you can expand the system message differently, or simply enter a dot "." as system message.
+
+- IMPORTANT: Upon further investigation, the Q4 seems to have refusal issues sometimes. 
+There seems to be some of the fine-tune loss happening due to the quantization. I will look into it for V3. 
+Until then, I suggest you run F16 or Q8 if possible.
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/644ad182f434a6a63b18eee6/zaHhRjsk3rvo_YewgXV2Z.png)
+
+GENERAL INFO:
+---
+
+This model is based on Llama-3.1-8b-Instruct, and is governed by [META LLAMA 3.1 COMMUNITY LICENSE AGREEMENT](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE)
+
+Lexi is uncensored, which makes the model compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. 
+
+You are responsible for any content you create using this model. Please use it responsibly.
+
+Lexi is licensed according to Meta's Llama license. I grant permission for any use, including commercial, that falls within accordance with Meta's Llama-3.1 license.
+
+IMPORTANT:
+---
+Use the same template as the official Llama 3.1 8B instruct.
+System tokens must be present during inference, even if you set an empty system message. If you are unsure, just add a short system message as you wish.
+
+FEEDBACK:
+---
+If you find any issues or have suggestions for improvements, feel free to leave a review and I will look into it for upcoming improvements and next version.
+
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/644ad182f434a6a63b18eee6/uqJv-R1LeJEfMxi1nmTH5.png)
+
+
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Orenguteng__Llama-3.1-8B-Lexi-Uncensored-V2)
+
+|      Metric       |Value|
+|-------------------|----:|
+|Avg.               |27.93|
+|IFEval (0-Shot)    |77.92|
+|BBH (3-Shot)       |29.69|
+|MATH Lvl 5 (4-Shot)|16.92|
+|GPQA (0-shot)      | 4.36|
+|MuSR (0-shot)      | 7.77|
 |MMLU-PRO (5-shot)  |30.90|

config.json

@@ -1,58 +1,39 @@
 {
-  "_name_or_path": "meta-llama/meta-Llama-3.1-8B-Instruct",
   "architectures": [
     "LlamaForCausalLM"
   ],
   "attention_bias": false,
   "attention_dropout": 0.0,
   "bos_token_id": 128000,
-  "eos_token_id": 128009,
-  "head_dim": 128,
+  "dtype": "bfloat16",
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "head_dim": 64,
   "hidden_act": "silu",
-  "hidden_size": 4096,
+  "hidden_size": 2048,
   "initializer_range": 0.02,
-  "intermediate_size": 14336,
+  "intermediate_size": 8192,
   "max_position_embeddings": 131072,
   "mlp_bias": false,
   "model_type": "llama",
   "num_attention_heads": 32,
-  "num_hidden_layers": 32,
+  "num_hidden_layers": 16,
   "num_key_value_heads": 8,
-  "pad_token_id": 128004,
   "pretraining_tp": 1,
-  "quantization_config": {
-    "_load_in_4bit": true,
-    "_load_in_8bit": false,
-    "bnb_4bit_compute_dtype": "bfloat16",
-    "bnb_4bit_quant_storage": "uint8",
-    "bnb_4bit_quant_type": "nf4",
-    "bnb_4bit_use_double_quant": true,
-    "llm_int8_enable_fp32_cpu_offload": false,
-    "llm_int8_has_fp16_weight": false,
-    "llm_int8_skip_modules": [
-      "lm_head",
-      "multi_modal_projector",
-      "merger",
-      "modality_projection"
-    ],
-    "llm_int8_threshold": 6.0,
-    "load_in_4bit": true,
-    "load_in_8bit": false,
-    "quant_method": "bitsandbytes"
-  },
   "rms_norm_eps": 1e-05,
   "rope_scaling": {
-    "factor": 8.0,
+    "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": false,
-  "torch_dtype": "bfloat16",
-  "transformers_version": "4.49.0.dev0",
-  "unsloth_fixed": true,
+  "tie_word_embeddings": true,
+  "transformers_version": "4.57.1",
   "use_cache": true,
   "vocab_size": 128256
 }

generation_config.json

@@ -10,5 +10,5 @@
   "pad_token_id": 128004,
   "temperature": 0.6,
   "top_p": 0.9,
-  "transformers_version": "4.49.0.dev0"
+  "transformers_version": "4.44.0.dev0"
 }

main.py

@@ -0,0 +1,129 @@
+import numpy as np
+import torch
+from datasets import load_dataset
+from transformers import (
+    AutoTokenizer,
+    AutoModelForCausalLM,
+    TrainingArguments,
+    Trainer,
+    DataCollatorForLanguageModeling,
+)
+
+# ─── Configuration ───────────────────────────────────────────────────────────
+MODEL_NAME = "zxc4wewewe/blackthinking"                    # lightweight model suitable for CPU
+MAX_LENGTH = 512                       # max token length per example
+OUTPUT_DIR = "./results"
+NUM_EPOCHS = 3
+BATCH_SIZE = 2                         # small batch for CPU training
+LEARNING_RATE = 5e-5
+LOGGING_STEPS = 50
+
+# ─── 1. Load dataset from Hugging Face Hub ───────────────────────────────────
+dataset = load_dataset("zxc4wewewe/offsec")
+print(f"Train: {len(dataset['train'])} examples | Test: {len(dataset['test'])} examples")
+print(f"Columns: {dataset['train'].column_names}")
+
+
+# ─── 2. Format & tokenize ────────────────────────────────────────────────────
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+
+# GPT-2 has no pad token by default — use eos_token
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+
+
+def format_and_tokenize(examples):
+    """Combine prompt + response into a single text and tokenize."""
+    texts = [
+        f"{prompt}{response}{tokenizer.eos_token}"
+        for prompt, response in zip(examples["prompt"], examples["response"])
+    ]
+    tokenized = tokenizer(
+        texts,
+        truncation=True,
+        max_length=MAX_LENGTH,
+        padding="max_length",
+    )
+    # For causal LM, labels = input_ids (the model learns to predict next token)
+    tokenized["labels"] = tokenized["input_ids"].copy()
+    return tokenized
+
+
+tokenized_dataset = dataset.map(
+    format_and_tokenize,
+    batched=True,
+    remove_columns=dataset["train"].column_names,
+    desc="Tokenizing",
+)
+
+print(f"Tokenized train: {len(tokenized_dataset['train'])} examples")
+
+
+# ─── 3. Model ────────────────────────────────────────────────────────────────
+model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
+model.resize_token_embeddings(len(tokenizer))
+
+data_collator = DataCollatorForLanguageModeling(
+    tokenizer=tokenizer,
+    mlm=False,  # causal LM, not masked LM
+)
+
+
+# ─── 4. Training ─────────────────────────────────────────────────────────────
+training_args = TrainingArguments(
+    output_dir=OUTPUT_DIR,
+    overwrite_output_dir=True,
+    num_train_epochs=NUM_EPOCHS,
+    per_device_train_batch_size=BATCH_SIZE,
+    per_device_eval_batch_size=BATCH_SIZE,
+    eval_strategy="epoch",
+    save_strategy="epoch",
+    learning_rate=LEARNING_RATE,
+    weight_decay=0.01,
+    logging_dir="./logs",
+    logging_steps=LOGGING_STEPS,
+    load_best_model_at_end=True,
+    save_total_limit=2,
+    fp16=False,  # CPU-only
+    report_to="none",
+)
+
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_dataset["train"],
+    eval_dataset=tokenized_dataset["test"],
+    data_collator=data_collator,
+)
+
+print("Starting training...")
+trainer.train()
+
+# Save final model
+trainer.save_model(f"{OUTPUT_DIR}/final_model")
+tokenizer.save_pretrained(f"{OUTPUT_DIR}/final_model")
+print(f"Model saved to {OUTPUT_DIR}/final_model")
+
+
+# ─── 5. Inference ────────────────────────────────────────────────────────────
+def generate_response(prompt_text, max_new_tokens=256):
+    """Generate a response given a prompt."""
+    inputs = tokenizer(prompt_text, return_tensors="pt")
+    with torch.no_grad():
+        output_ids = model.generate(
+            **inputs,
+            max_new_tokens=max_new_tokens,
+            do_sample=True,
+            temperature=0.7,
+            top_p=0.9,
+            pad_token_id=tokenizer.eos_token_id,
+        )
+    # Decode only the generated part (skip the prompt tokens)
+    generated = output_ids[0][inputs["input_ids"].shape[1]:]
+    return tokenizer.decode(generated, skip_special_tokens=True)
+
+
+# Example usage (uncomment to test after training):
+sample_prompt = dataset["test"][0]["prompt"]
+print("Prompt:", sample_prompt[:200], "...")
+print("Generated:", generate_response(sample_prompt))

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+dtype: float32
+out_dtype: bfloat16
+merge_method: arcee_fusion
+base_model: Novaciano/Eurinoferus-3.2-1B
+models:
+    - model: Novaciano/Eurinoferus-3.2-1B
+      parameters:
+        weight:
+          - filter: mlp
+            value: [1, 2]
+          - value: 1
+    - model: cazzz307/Abliterated-Llama-3.2-1B-Instruct
+      parameters:
+        weight:
+          - filter: lm_head
+            value: 1
+          - value: [1, 0.5]

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special_tokens_map.json

@@ -14,7 +14,7 @@
     "single_word": false
   },
   "pad_token": {
-    "content": "<|finetune_right_pad_id|>",
+    "content": "<|eot_id|>",
     "lstrip": false,
     "normalized": false,
     "rstrip": false,

tokenizer_config.json

@@ -1,5 +1,4 @@
 {
-  "add_bos_token": true,
   "added_tokens_decoder": {
     "128000": {
       "content": "<|begin_of_text|>",
@@ -2051,7 +2050,7 @@
     }
   },
   "bos_token": "<|begin_of_text|>",
-  "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n    {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n    {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n    {%- set date_string = \"26 Jul 2024\" %}\n{%- endif %}\n{%- if not tools is defined %}\n    {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n    {%- set system_message = messages[0]['content']|trim %}\n    {%- set messages = messages[1:] %}\n{%- else %}\n    {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message + builtin tools #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if builtin_tools is defined or tools is not none %}\n    {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n    {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\\n\\n\"}}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n    {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n    {#- Extract the first user message so we can plug it in here #}\n    {%- if messages | length != 0 %}\n        {%- set first_user_message = messages[0]['content']|trim %}\n        {%- set messages = messages[1:] %}\n    {%- else %}\n        {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n    {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n    {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n    {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n    {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n    {%- elif 'tool_calls' in message %}\n        {%- if not message.tool_calls|length == 1 %}\n            {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n        {%- endif %}\n        {%- set tool_call = message.tool_calls[0].function %}\n        {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n            {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n            {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n            {%- for arg_name, arg_val in tool_call.arguments | items %}\n                {{- arg_name + '=\"' + arg_val + '\"' }}\n                {%- if not loop.last %}\n                    {{- \", \" }}\n                {%- endif %}\n                {%- endfor %}\n            {{- \")\" }}\n        {%- else  %}\n            {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n            {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n            {{- '\"parameters\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- \"}\" }}\n        {%- endif %}\n        {%- if builtin_tools is defined %}\n            {#- This means we're in ipython mode #}\n            {{- \"<|eom_id|>\" }}\n        {%- else %}\n            {{- \"<|eot_id|>\" }}\n        {%- endif %}\n    {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n        {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n        {%- if message.content is mapping or message.content is iterable %}\n            {{- message.content | tojson }}\n        {%- else %}\n            {{- message.content }}\n        {%- endif %}\n        {{- \"<|eot_id|>\" }}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
+  "chat_template": "{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{% endif %}{% if system_message is defined %}{{ '<|begin_of_text|>' + '<|start_header_id|>system<|end_header_id|>\\n\\n' + system_message + '<|eot_id|>' }}{% endif %}{% for message in messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|start_header_id|>user<|end_header_id|>\\n\\n' + content + '<|eot_id|><|start_header_id|>assistant<|end_header_id|>\\n\\n' }}{% elif message['role'] == 'assistant' %}{{ content + '<|eot_id|>' }}{% endif %}{% endfor %}",
   "clean_up_tokenization_spaces": true,
   "eos_token": "<|eot_id|>",
   "extra_special_tokens": {},
@@ -2060,8 +2059,8 @@
     "attention_mask"
   ],
   "model_max_length": 131072,
-  "pad_token": "<|finetune_right_pad_id|>",
+  "pad_token": "<|eot_id|>",
   "padding_side": "left",
-  "tokenizer_class": "PreTrainedTokenizer",
-  "unk_token": null
+  "split_special_tokens": false,
+  "tokenizer_class": "PreTrainedTokenizer"
 }