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Model: serving-d-cause/writing-roleplay-20k-context-nemo-12b-v1.0
Source: Original Platform

.gitattributes

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README.md

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+---
+language:
+- en
+license: apache-2.0
+base_model: mistralai/Mistral-Nemo-Base-2407
+tags:
+- text-generation
+- roleplay
+- storytelling
+- finetune
+- qlora
+- chatml
+library_name: transformers
+pipeline_tag: text-generation
+datasets:
+- openerotica/mixed-rp
+- anthracite-org/stheno-filtered-v1.1
+- anthracite-org/kalo_misc_part2
+- anthracite-org/kalo_opus_misc_240827
+- anthracite-org/kalo-opus-instruct-22k-no-refusal
+- Chaser-cz/sonnet35-charcard-roleplay-sharegpt
+- jondurbin/airoboros-3.2
+---
+
+If you like this model, consider joining my discord to provide feedback: https://discord.gg/QXdn8hWSkY
+
+This is a storywriting and roleplay model with a significant amount of self generated long context multiturn roleplay.
+
+I downloaded a bit under a thousand cards from chub.ai, and created a synthetic roleplay for each card. I batched as many turns as I could in 4k token chunks in order to maintain coherency over longer context. There was a lot of cleaning and validation between each batch, so a lot of examples were "lost,"  but the final output seems to be very good quality. The longest conversation is about 20k tokens, and I plan to extend this further as well as broaden the dataset with more examples. The first 4k tokens were generated with Command-R-Plus, with the remainder generated with byroneverson/Mistral-Small-Instruct-2409-abliterated.
+
+Next, I downloaded the prompt backup from this site, and used them as a seed for some storywriting data:
+
+https://aetherroom.club/whats-new#backup-update
+
+I went over it twice with Command-R-Plus. The first time, having it basically write the first draft of the output, the second improving and extending the length of the original output.
+
+Also included was a subset of the following datasets:
+
+anthracite-org/stheno-filtered-v1.1
+
+anthracite-org/kalo_misc_part2
+
+anthracite-org/kalo_opus_misc_240827
+
+anthracite-org/kalo-opus-instruct-22k-no-refusal
+
+Chaser-cz/sonnet35-charcard-roleplay-sharegpt
+
+(A very small subset) jondurbin/airoboros-3.2
+
+And some various other data, viewable at openerotica/mixed-rp
+
+Every line of data was run through a large model in order to filter for low quality, repetition, and underage content.
+
+There is a LOT more I can do to improve the dataset used to create this model. As of now, the storywriting data is all single shot, where as I'd like to create some examples of "contunue the story in x direction..." and "rewrite it adding/removing these elements".  More stable diffusion roleplay prompting data could also be helpful. Roleplay conversations should be exteneded to 32k and beyond.
+
+
+[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
+
+```yaml
+base_model: mistralai/Mistral-Nemo-Base-2407
+model_type: AutoModelForCausalLM
+tokenizer_type: AutoTokenizer
+
+load_in_8bit: false
+load_in_4bit: true
+strict: false
+
+datasets:
+  - path: openerotica/mixed-rp
+    type: sharegpt
+    conversation: chatml
+
+chat_template: chatml
+adapter: qlora
+lora_r: 128
+lora_alpha: 256
+lora_modules_to_save: [embed_tokens, lm_head]
+lora_dropout: 0.05
+lora_target_linear: true
+lora_target_modules:
+  - gate_proj
+  - down_proj
+  - up_proj
+  - q_proj
+  - v_proj
+  - k_proj
+  - o_proj
+
+dataset_prepared_path:
+val_set_size: 0.01
+output_dir: /workspace/axolotl/mixed-rp-mistral-nemo
+
+sequence_len: 20000
+sample_packing: true
+pad_to_sequence_len: true
+
+wandb_project: mistral-2
+wandb_watch:
+wandb_run_id:
+wandb_log_model:
+
+gradient_accumulation_steps: 2
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_torch
+lr_scheduler: cosine
+learning_rate: 1e-5
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+fp16:
+tf32: false
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+early_stopping_patience:
+resume_from_checkpoint:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_steps: 100
+evals_per_epoch: 4
+eval_table_size:
+saves_per_epoch: 1
+save_total_limit: 2
+save_steps:
+debug:
+deepspeed:
+weight_decay: 0.1
+special_tokens:
+  eos_token: "<|im_end|>"
+  pad_token: "<pad>"
+  bos_token: "<s>"
+  unk_token: "<unk>"
+tokens:
+  - "<|im_start|>"
+
+# fsdp:
+#   - full_shard
+#   - auto_wrap
+# fsdp_config:
+#   fsdp_limit_all_gathers: true
+#   fsdp_sync_module_states: true
+#   fsdp_offload_params: true
+#   fsdp_use_orig_params: false
+#   fsdp_cpu_ram_efficient_loading: true
+#   fsdp_transformer_layer_cls_to_wrap: MixtralSparseMoeBlock
+#   fsdp_state_dict_type: FULL_STATE_DICT
+#   fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+#   fsdp_sharding_strategy: FULL_SHARD
+#   fsdp_forward_prefetch: false
+#   fsdp_backward_prefetch: BACKWARD_PRE

chat_template.jinja

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+{% if not add_generation_prompt is defined %}
+  {% set add_generation_prompt = false %}
+{% endif %}
+
+{% for message in messages %}
+  {{ '<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n' }}
+{% endfor %}
+
+{% if add_generation_prompt %}
+  {{ '<|im_start|>assistant\n' }}
+{% endif %}

config.json

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+{
+  "_name_or_path": "mistralai/Mistral-Nemo-Base-2407",
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 131072,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 1024000,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 40,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.45.1",
+  "use_cache": false,
+  "vocab_size": 131074
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "do_sample": true,
+  "eos_token_id": 2,
+  "transformers_version": "4.45.1"
+}

handler.py

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+import os
+from typing import Any, Dict, List
+from transformers import AutoTokenizer, AutoModelForCausalLM, TextGenerationPipeline
+
+# Ensure your template includes escaped newlines correctly
+CHAT_TEMPLATE = (
+    "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}"
+    "{% for message in messages %}"
+    "{{'<|im_start|>' + message['role'] + '\\n' + message['content'] + '<|im_end|>' + '\\n'}}"
+    "{% endfor %}"
+    "{% if add_generation_prompt %}{{ '<|im_start|>assistant\\n' }}{% endif %}"
+)
+
+class EndpointHandler:
+    def __init__(self, model_dir: str, **kwargs: Any):
+        # Load tokenizer and model from provided model_dir
+        self.tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
+        # Assign the chat_template under 'default'
+        self.tokenizer.chat_template = {"default": CHAT_TEMPLATE}
+        self.model = AutoModelForCausalLM.from_pretrained(
+            model_dir, trust_remote_code=True, device_map="auto"
+        )
+        self.pipeline = TextGenerationPipeline(
+            model=self.model, tokenizer=self.tokenizer, return_full_text=False
+        )
+
+    def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        # Validate input structure
+        messages: List[Dict[str, str]] = data.get("messages")
+        if messages is None:
+            raise ValueError("Request body must include 'messages' array.")
+
+        # Format prompt with proper controlling flag
+        inputs = self.tokenizer.apply_chat_template(
+            messages=messages, add_generation_prompt=True, return_tensors=None
+        )
+        # Generate text
+        gen = self.pipeline(inputs, max_new_tokens=data.get("parameters", {}).get("max_new_tokens", 128))
+        content = gen[0]["generated_text"]
+
+        return {"choices": [{"message": {"role": "assistant", "content": content}}]}
+

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