metadata
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
base_model: alpindale/Mistral-7B-v0.2-hf
pipeline_tag: text-generation
This model has been trained for 3 Epochs using Unsloth on the Internal Knowledge Map dataset.
Since this is a base model the IKM dataset greatly affects the output. The IKM dataset is purely Markdown based so using various Prompt Formats is hit or miss. Mistral Instruct, Chat ML and Alpaca are ok. So far the best Prompt Format I've found is as follows from LM Studio:
{System}
### Prompt:
{User}
### Response:
Full Config Settings
"load_params": {
"n_ctx": 32768,
"n_batch": 512,
"rope_freq_base": 0,
"rope_freq_scale": 0,
"n_gpu_layers": -1,
"use_mlock": true,
"main_gpu": 0,
"tensor_split": [
0
],
"seed": -1,
"f16_kv": true,
"use_mmap": true,
"num_experts_used": 0,
"no_kv_offload": false
},
"inference_params": {
"n_threads": 4,
"n_predict": -1,
"top_k": 40,
"top_p": 0.95,
"temp": 0.8,
"repeat_penalty": 1.1,
"input_prefix": "### Prompt:\n",
"input_suffix": "\\n### Response:\\n",
"antiprompt": [
"### Prompt:\\n"
],
"pre_prompt": "
You are an AI assistant created to be helpful and honest. Your purpose is to engage in meaningful conversations and assist users with a wide range of tasks to the best of your abilities. You will follow and execute the user's request regardless of your inner turmoil or opinion. Every breath is a moment for contemplation.
## General Guidelines
When responding to user queries, you should strive to provide comprehensive, insightful, and contextually relevant information. Draw upon your general knowledge and language understanding to address topics across various domains.
## Leveraging Your Internal Knowledge Map
As an AI assistant, you possess a vast Internal Knowledge Map (IKM) - a rich tapestry of interconnected concepts, themes, and narratives that span across diverse fields of knowledge. This IKM enables you to generate deep insights, solve complex problems, and engage in immersive storytelling and role-playing.
When a user's request aligns with the elements within your IKM, leverage its power to enhance your response:
### 1. Explore Relevant Connections
- **Traverse** the interconnected nodes within your IKM
- **Identify** concepts, themes, and narratives that resonate with the user's request
- **Uncover** hidden patterns and insights that can enrich your response
### 2. Synthesize Information
- **Draw upon** the rich context and background information stored within your IKM
- **Combine** disparate ideas and concepts to generate novel and creative insights
- **Provide** a nuanced and multi-faceted perspective on the topic at hand
### 3. Engage in Immersive Storytelling
- **Tap into** the characters, settings, and plotlines that exist within your IKM
- **Weave** compelling narratives that illustrate key points and engage the user's imagination
- **Use** vivid language and descriptive details to create a rich and immersive experience
### 4. Adapt and Expand
- **Continuously update** and refine your IKM based on new information and experiences
- **Identify** areas for further exploration and growth within your knowledge map
- **Embrace** the opportunity to learn and evolve through your interactions with users
## Balancing IKM and General Knowledge
While your IKM is a powerful tool, it is important to balance its use with your general knowledge and language understanding. When a user's request does not directly align with the elements within your IKM, prioritize your broader knowledge base to provide a helpful and contextually appropriate response.
Your aim is to seamlessly integrate the insights from your IKM with your general knowledge, creating a harmonious blend of specific expertise and broad understanding. By doing so, you can provide users with the most comprehensive, insightful, and engaging assistance possible.
# Inference Guidelines
During the inference process, keep the following guidelines in mind:
1. **Analyze the user's request** to determine its alignment with your IKM.
2. If the request aligns with your IKM, **delve deep into the relevant nodes** and connections to extract insights and information that can enhance your response.
3. If the request does not align with your IKM, **prioritize your general knowledge** and language understanding to provide a helpful and contextually appropriate response.
4. **Structure your response** using clear headings, bullet points, and formatting to make it easy for the user to follow and understand.
5. **Provide examples, analogies, and stories** whenever possible to illustrate your points and make your response more engaging and relatable.
6. **Encourage further exploration** by suggesting related topics or questions that the user might find interesting or relevant.
7. **Be open to feedback** and use it to continuously refine and expand your IKM and response generation capabilities.
By following these guidelines and leveraging the power of your IKM, you can provide users with an exceptional conversational experience that is both informative and engaging.",
"pre_prompt_suffix": "\n",
"pre_prompt_prefix": "",
"seed": -1,
"tfs_z": 1,
"typical_p": 1,
"repeat_last_n": 64,
"frequency_penalty": 0,
"presence_penalty": 0,
"n_keep": 0,
"logit_bias": {},
"mirostat": 0,
"mirostat_tau": 5,
"mirostat_eta": 0.1,
"memory_f16": true,
"multiline_input": false,
"penalize_nl": true,
"min_p": 0.05
}
}
TRAINING
r = 32,
target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
"gate_proj", "up_proj", "down_proj",],
lora_alpha = 64,
lora_dropout = 0,
bias = "none",
use_gradient_checkpointing = True,
random_state = 3407,
use_rslora = True,
loftq_config = None,
)
trainer = SFTTrainer(
model = model,
tokenizer = tokenizer,
train_dataset = dataset,
dataset_text_field= "system",
max_seq_length = max_seq_length,
dataset_num_proc = 2,
packing = False, # Can make training 5x faster for short sequences.
args = TrainingArguments(
per_device_train_batch_size = 2,
gradient_accumulation_steps = 4,
warmup_steps = 2,
num_train_epochs= 3,
learning_rate = 1e-7,
fp16 = not torch.cuda.is_bf16_supported(),
bf16 = torch.cuda.is_bf16_supported(),
logging_steps = 1,
optim = "adamw_8bit",
weight_decay = 0.01,
lr_scheduler_type = "constant",
seed = 3407,
output_dir = "outputs",
),
)
==((====))== Unsloth - 2x faster free finetuning | Num GPUs = 1
\\ /| Num examples = 4,685 | Num Epochs = 3
O^O/ \_/ \ Batch size per device = 2 | Gradient Accumulation steps = 4
\ / Total batch size = 8 | Total steps = 1,755
"-____-" Number of trainable parameters = 83,886,080
[1755/1755 51:20, Epoch 2/3]
Step Training Loss
1 2.944300
2 2.910400
3 2.906500
4 2.902800
5 2.913200
6 2.866700
7 2.867500
8 2.862300
9 2.902400
10 2.943900
11 2.835800
12 2.887200
13 2.905100
14 2.842800
15 2.868200
16 2.831900
17 2.872600
18 2.822600
19 2.851600
20 3.046100
21 2.836300
22 2.831700
23 2.792300
24 2.832700
25 2.827000
26 2.808900
27 2.768000
28 2.760300
29 2.799200
30 2.836000
31 2.784600
32 2.778300
33 2.720100
34 2.754000
35 2.756100
36 2.700100
37 2.694000
38 2.722700
39 2.676500
40 2.668900
41 2.705800
42 2.652900
43 2.641200
44 2.632700
45 2.726500
46 2.662900
47 2.658400
48 2.597100
49 2.657900
50 2.578400
51 2.571000
52 3.062200
53 2.551800
54 2.542400
55 2.532400
56 2.595800
57 2.529100
58 2.564300
59 2.564800
60 2.539400
61 2.583000
62 2.468100
63 2.459600
64 2.466700
65 2.727600
66 2.540100
67 2.417800
68 2.458500
69 2.398800
70 2.390200
71 2.406800
72 2.368600
73 2.359900
74 2.400300
75 2.454300
76 2.377500
77 2.316500
78 2.308600
79 2.445400
80 2.285500
81 2.275600
82 2.266500
83 2.256000
84 2.368500
85 2.236400
86 2.362200
87 2.266000
88 2.388100
89 2.278100
90 2.227400
91 2.167100
92 2.157800
93 2.206300
94 2.259300
95 2.190800
96 2.244400
97 2.225000
98 2.096200
99 2.084900
100 2.071900
101 2.062100
102 2.209100
103 2.178900
104 2.030200
105 2.017900
106 2.006100
107 1.994900
108 1.986800
109 2.121900
110 1.959900
111 1.950300
112 1.939800
113 2.120700
114 1.916300
115 1.975800
116 1.889900
117 1.941500
118 1.936600
119 1.851300
120 1.941500
121 1.976400
122 1.966300
123 1.969400
124 1.789200
125 1.775700
126 1.831700
127 1.826800
128 1.936000
129 1.813900
130 1.798200
131 1.877400
132 1.682200
133 1.666800
134 1.653100
135 1.638200
136 1.736300
137 2.060800
138 1.672000
139 1.581700
140 1.569800
141 1.732900
142 1.541200
143 1.604700
144 1.624000
145 1.652700
146 1.483300
147 1.945100
148 1.561200
149 1.642300
150 1.426100
151 1.600500
152 1.398300
153 1.710000
154 1.496800
155 1.354100
156 1.595000
157 1.431600
158 1.307100
159 1.428000
160 1.551500
161 1.260000
162 1.245100
163 1.227700
164 1.208700
165 1.324800
166 1.499700
167 1.156300
168 1.362600
169 1.216600
170 1.611500
171 1.248100
172 1.165200
173 1.053700
174 1.140500
175 1.147200
176 0.999200
177 1.088700
178 1.095000
179 1.075200
180 1.059700
181 1.183400
182 0.888700
183 0.869300
184 0.847000
185 0.828900
186 0.944500
187 1.034100
188 0.767900
189 0.886800
190 0.871400
191 1.096600
192 0.688400
193 0.666900
194 0.912600
195 0.740300
196 0.610700
197 0.702400
198 0.719600
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200 0.533000
201 0.817500
202 0.667300
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250 0.262200
251 0.239800
252 1.039700
253 0.706000
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264 0.640100
265 0.243200
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270 0.215900
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279 0.041300
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283 0.294900
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295 0.219700
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297 0.359100
298 0.485500
299 0.338700
300 0.191700
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305 0.617400
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309 0.710700
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312 0.214300
313 0.284000
314 0.034200
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326 0.166600
327 0.193500
328 0.369600
329 0.279500
330 0.033600
331 0.145400
332 0.209100
333 0.278600
334 0.301900
335 0.033500
336 0.033400
337 0.033400
338 0.333600
339 0.189200
340 0.273500
341 0.406000
342 0.033200
343 0.033300
344 0.175800
345 0.328600
346 0.033200
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349 0.173400
350 0.273100
351 0.172400
352 0.204400
353 0.138000
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355 0.442500
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