Nexus-IKM-Hermes-2-Pro-Mistral-7B-Pytorch

This model has been trained for 2 epochs using Unsloth on the Internal Knowledge Map dataset.

System Prompt for Best Results (The use of Markdown is VERY IMPORTANT)

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.

---

    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,
)

==((====))==  Unsloth - 2x faster free finetuning | Num GPUs = 1
   \\   /|    Num examples = 3,555 | Num Epochs = 2
O^O/ \_/ \    Batch size per device = 4 | Gradient Accumulation steps = 4
\        /    Total batch size = 16 | Total steps = 444
 "-____-"     Number of trainable parameters = 83,886,080
 [444/444 25:17, Epoch 1/2]
Step	Training Loss
1	3.133100
2	3.086100
3	3.045000
4	3.075100
5	3.086000
6	3.042100
7	3.018100
8	3.036100
9	2.986900
10	2.990600
11	2.949400
12	2.933200
13	2.899800
14	2.885900
15	2.928400
16	2.855700
17	2.805000
18	2.787100
19	2.807400
20	2.765600
21	2.794500
22	2.758400
23	2.753700
24	2.757400
25	2.669900
26	2.653900
27	2.708400
28	2.705100
29	2.695900
30	2.590100
31	2.615900
32	2.577500
33	2.571700
34	2.596400
35	2.570700
36	2.558600
37	2.524600
38	2.640500
39	2.506400
40	2.521900
41	2.519800
42	2.459700
43	2.388900
44	2.425400
45	2.387800
46	2.360600
47	2.376000
48	2.391600
49	2.321100
50	2.357600
51	2.325800
52	2.311800
53	2.255600
54	2.313900
55	2.200900
56	2.250800
57	2.242500
58	2.173000
59	2.261000
60	2.150500
61	2.162500
62	2.086800
63	2.178500
64	2.085600
65	2.068800
66	2.146500
67	2.001800
68	2.037600
69	2.009000
70	1.983300
71	1.931400
72	1.990400
73	1.944700
74	1.972700
75	2.002400
76	2.022400
77	1.900500
78	1.843100
79	1.887400
80	1.970700
81	1.820800
82	1.853900
83	1.744200
84	1.831400
85	1.768900
86	2.006100
87	1.681900
88	1.750000
89	1.628100
90	1.586900
91	1.567900
92	1.554500
93	1.830800
94	1.512500
95	1.592400
96	1.518600
97	1.593700
98	1.454100
99	1.497200
100	1.319700
101	1.363300
102	1.414300
103	1.343900
104	1.363500
105	1.449000
106	1.510100
107	1.268600
108	1.156600
109	1.075100
110	1.137200
111	1.020700
112	0.993600
113	1.195200
114	0.993300
115	1.072100
116	1.116900
117	1.184100
118	1.102600
119	1.083800
120	0.852100
121	1.023600
122	1.051200
123	1.270500
124	0.856200
125	1.089500
126	0.686800
127	0.800300
128	0.662400
129	0.688000
130	0.554400
131	0.737200
132	0.802900
133	0.538200
134	0.562000
135	0.516800
136	0.497200
137	0.611100
138	0.581200
139	0.442000
140	0.355200
141	0.473200
142	0.559600
143	0.683700
144	0.355300
145	0.343000
146	0.525300
147	0.442100
148	0.452900
149	0.478800
150	0.311300
151	0.535500
152	0.552600
153	0.252800
154	0.479200
155	0.539500
156	0.477200
157	0.283000
158	0.265100
159	0.352000
160	0.268500
161	0.711900
162	0.411300
163	0.377100
164	0.360500
165	0.311000
166	0.490800
167	0.269300
168	0.409600
169	0.147800
170	0.144600
171	0.223600
172	0.615300
173	0.218900
174	0.136400
175	0.133200
176	0.263200
177	0.363600
178	0.127700
179	0.238900
180	0.276200
181	0.306400
182	0.122000
183	0.302400
184	0.049500
185	0.406500
186	0.246400
187	0.429900
188	0.216900
189	0.320700
190	0.472800
191	0.159900
192	0.287500
193	0.334400
194	0.136100
195	0.233400
196	0.164100
197	0.196100
198	0.153300
199	0.251000
200	0.087500
201	0.083000
202	0.104900
203	0.157700
204	0.080300
205	0.280500
206	0.372100
207	0.150400
208	0.112900
209	0.265400
210	0.075800
211	0.082700
212	0.343000
213	0.081900
214	0.360400
215	0.261200
216	0.072000
217	0.249400
218	0.211600
219	0.304500
220	0.289300
221	0.209400
222	0.067800
223	0.144500
224	0.078600
225	0.143500
226	0.377800
227	0.222300
228	0.279800
229	0.063400
230	0.120400
231	0.214000
232	0.121600
233	0.360400
234	0.168600
235	0.206300
236	0.075800
237	0.033800
238	0.059700
239	0.227500
240	0.212800
241	0.186600
242	0.223400
243	0.033600
244	0.204600
245	0.033600
246	0.600600
247	0.105800
248	0.198400
249	0.255100
250	0.226500
251	0.104700
252	0.128700
253	0.088300
254	0.158600
255	0.033200
256	0.261900
257	0.320500
258	0.140100
259	0.266200
260	0.087300
261	0.085400
262	0.240300
263	0.308800
264	0.033000
265	0.120300
266	0.156400
267	0.083200
268	0.199200
269	0.052000
270	0.116600
271	0.144000
272	0.237700
273	0.214700
274	0.180600
275	0.334200
276	0.032800
277	0.101700
278	0.078800
279	0.163300
280	0.032700
281	0.098000
282	0.126500
283	0.032600
284	0.110000
285	0.063500
286	0.382900
287	0.193200
288	0.264400
289	0.119000
290	0.189500
291	0.274900
292	0.102100
293	0.101000
294	0.197300
295	0.083300
296	0.153000
297	0.057500
298	0.335000
299	0.150400
300	0.044300
301	0.317200
302	0.073700
303	0.217200
304	0.043100
305	0.061800
306	0.100500
307	0.088800
308	0.153700
309	0.157200
310	0.086700
311	0.114000
312	0.077200
313	0.092000
314	0.167700
315	0.237000
316	0.215800
317	0.058100
318	0.077200
319	0.162900
320	0.122400
321	0.171100
322	0.142000
323	0.032100
324	0.098500
325	0.059400
326	0.038500
327	0.089000
328	0.123200
329	0.190200
330	0.051700
331	0.087400
332	0.198400
333	0.073500
334	0.073100
335	0.176600
336	0.186100
337	0.183000
338	0.106100
339	0.064700
340	0.136500
341	0.085600
342	0.115400
343	0.106000
344	0.065800
345	0.143100
346	0.137300
347	0.251000
348	0.067200
349	0.181600
350	0.084600
351	0.108800
352	0.114600
353	0.043200
354	0.241500
355	0.031800
356	0.150500
357	0.063700
358	0.036100
359	0.158100
360	0.045700
361	0.120200
362	0.035800
363	0.050200
364	0.031700
365	0.044000
366	0.035400
367	0.035300
368	0.162500
369	0.044400
370	0.132700
371	0.054300
372	0.049100
373	0.031500
374	0.038000
375	0.084900
376	0.059000
377	0.034500
378	0.049200
379	0.058100
380	0.122700
381	0.096400
382	0.034300
383	0.071700
384	0.059300
385	0.048500
386	0.051000
387	0.063000
388	0.131400
389	0.031100
390	0.076700
391	0.072200
392	0.146300
393	0.031000
394	0.031000
395	0.099200
396	0.049000
397	0.104100
398	0.087400
399	0.097100
400	0.069800
401	0.034900
402	0.035300
403	0.057400
404	0.058000
405	0.041100
406	0.083400
407	0.090000
408	0.098600
409	0.106100
410	0.052600
411	0.057800
412	0.085500
413	0.061600
414	0.034000
415	0.079700
416	0.036800
417	0.034600
418	0.073800
419	0.047900
420	0.041100
421	0.046300
422	0.030600
423	0.064200
424	0.045900
425	0.045600
426	0.032900
427	0.048800
428	0.041700
429	0.048200
430	0.035800
431	0.058200
432	0.044100
433	0.033400
434	0.046100
435	0.042800
436	0.034900
437	0.045800
438	0.055800
439	0.030300
440	0.059600
441	0.030200
442	0.052700
443	0.030200
444	0.035600